1 Introduction

In this RMarkdown file, the data-analysis for the manuscript “Multisystem inflammatory syndrome in children related to COVID-19: a systematic review” is described. The complete data-analysis can be reproduced from the data collection sheet (in .xls format), provided in the supplementary data.

knitr::opts_chunk$set(cache = FALSE, warning = FALSE, message = FALSE)
options(digits = 3)
library(tidyverse)
require(readxl)
require(httr)
require(reshape2)
require(broom)
require(RColorBrewer)
require(scales)
require(ggrepel)
require(gridExtra)
require(ggExtra)
library(ggbeeswarm)
require(ggpubr)
library(cowplot)
library(naniar)
require(DT)
require(zoo)
require(psych)
library(skimr)
library(UpSetR)
library(see)
library(wesanderson)

options(scipen=999)

co_hb <- 12
co_neutrophilia <- 8000
co_CRP <- 10
co_lympho <- 1250
co_fibrino <- 400
co_Ddim <- 250
co_ferritin <- 300
co_albu <- 34
co_PCT <- 0.49
co_LDH <- 280
co_IL6 <- 16.4
co_ESR <- 22
co_BNP <- 100
co_NTproBNP <- 400
co_tropo <- 40
co_WBC <- 11000
co_platelet <- 150000
co_sodium <- 135
#input = df_cohort_controls
#find = "max"
#param = "CRP"

collapse_labvals_cohort <- function(input, find, param){
  if (find == "max"){
    df <- input %>% select(contains(param) | contains("cohort_id") | contains("cohort_type") | contains("tot_cases_n"))
    print("Column extracted from cohorts:")
    print(colnames(df))
    df_med <- df %>% select(contains("med"))
    df_med <- type_convert(df_med)
    df_med <- df_med %>% mutate_all(funs(replace_na(., -999)))
   # colnames(df_med)[max.col(df_med,ties.method="first")]
    df_med <- df_med %>% mutate(med = as.numeric(apply(df_med, 1, max)))
    
    df_min <- df %>% select(contains("Q1"))
    df_min <- type_convert(df_min)
    df_min <- df_min %>% mutate_all(funs(replace_na(., 0)))
    #colnames(df_min)[max.col(df_min,ties.method="first")]
    df_min <- df_min %>% mutate(min = as.numeric(apply(df_min, 1, max)))
    
    df_max <- df %>% select(contains("Q3"))
    df_max <- type_convert(df_max)
    df_max <- df_max %>% mutate_all(funs(replace_na(., 0)))
    #colnames(df_max)[max.col(df_max,ties.method="first")]
    df_max <- df_max %>% mutate(max = as.numeric(apply(df_max, 1, max)))
    
    df_full <- cbind(df %>% select(cohort_id, cohort_type, tot_cases_n), df_med %>% select(med), df_min %>% select(min), df_max %>% select(max))
    df_full[df_full == -999] <- NA
    names(df_full)[names(df_full) == 'max'] <- paste0(param, "_max")
    names(df_full)[names(df_full) == 'min'] <- paste0(param, "_min")
    names(df_full)[names(df_full) == 'med'] <- paste0(param, "_med")
    df_full$data_descr <- "IQR"
    df_full$cohort_id <- paste0(df_full$cohort_id, " (n = ", as.character(df_full$tot_cases_n),")")
    write.csv(df_full, paste0("./data/cohort_", param, ".csv"))
    print(datatable(df_full, caption = paste0("overview of ", param)))
    return(df_full)
  }
    else if (find == "min"){
    df <- input %>% select(contains(param) | contains("cohort_id") | contains("cohort_type") | contains("tot_cases_n"))
    print("Column extracted from cohorts:")
    print(colnames(df))
    df_med <- df %>% select(contains("med"))
    df_med <- type_convert(df_med)
    df_med <- df_med %>% mutate_all(funs(replace_na(., 1e6)))
   # colnames(df_med)[max.col(df_med,ties.method="first")]
    df_med <- df_med %>% mutate(med = as.numeric(apply(df_med, 1, min)))
    
    df_min <- df %>% select(contains("Q1"))
    df_min <- type_convert(df_min)
    df_min <- df_min %>% mutate_all(funs(replace_na(., 1e6)))
    #colnames(df_min)[max.col(df_min,ties.method="first")]
    df_min <- df_min %>% mutate(min = as.numeric(apply(df_min, 1, min)))
    
    df_max <- df %>% select(contains("Q3"))
    df_max <- type_convert(df_max)
    df_max <- df_max %>% mutate_all(funs(replace_na(., 1e6)))
    #colnames(df_max)[max.col(df_max,ties.method="first")]
    df_max <- df_max %>% mutate(max = as.numeric(apply(df_max, 1, min)))
    
    df_full <- cbind(df %>% select(cohort_id, cohort_type, tot_cases_n), df_med %>% select(med), df_min %>% select(min), df_max %>% select(max))
    df_full[df_full == 1e6] <- NA
    names(df_full)[names(df_full) == 'max'] <- paste0(param, "_max")
    names(df_full)[names(df_full) == 'min'] <- paste0(param, "_min")
    names(df_full)[names(df_full) == 'med'] <- paste0(param, "_med")
    df_full$data_descr <- "IQR"
    df_full$cohort_id <- paste0(df_full$cohort_id, " (n = ", as.character(df_full$tot_cases_n),")")
    write.csv(df_full, paste0("./data/cohort_", param, ".csv"))
    print(datatable(df_full, caption = paste0("overview of ", param)))
    return(df_full)
  }
}

#input = df_singlecases
#find = "max"
#param = "CRP"

collapse_labvals_single <- function(input, find, param){
  if (find == "max"){
    df <- input %>% select(contains(param) | contains("cohort_id"))
    print("Column extracted from single cases:")
    print(colnames(df))
    df_coll <- df %>% mutate_all(funs(replace_na(., -999)))
    df_coll <- type_convert(df_coll)
   # colnames(df_med)[max.col(df_med,ties.method="first")]
    df_coll <- df_coll %>% mutate(max = as.numeric(apply(df_coll, 1, max)))
    
    df_coll[df_coll == -999] <- NA
    names(df_coll)[names(df_coll) == 'max'] <- paste0(param, "_max")
    df_coll$data_descr <- "IQR"
    df_coll$cohort_id <- paste0("single cases (n = ", as.character(n_single_cases),")")
    write.csv(skim(df_coll), paste0("./data/singlecases_", param, ".csv"))
    return(df_coll)
  }
    else if (find == "min"){
    df <- input %>% select(contains(param) | contains("cohort_id"))
    print("Column extracted from single cases:")
    print(colnames(df))
    df_coll <- df %>% mutate_all(funs(replace_na(., 1e6)))
   # colnames(df_med)[max.col(df_med,ties.method="first")]
    df_coll <- df_coll %>% mutate(min = as.numeric(apply(df_coll, 1, min)))
    
    df_coll[df_coll == 1e6] <- NA
    names(df_coll)[names(df_coll) == 'min'] <- paste0(param, "_min")
    df_coll$cohort_id <- paste0("single cases (n = ", as.character(n_single_cases),")")
    write.csv(skim(df_coll), paste0("./data/singlecases_", param, ".csv"))
    return(df_coll)
  }
}


moveme <- function (df, movecommand) {
  invec <- names(df)
  
  movecommand <- lapply(strsplit(strsplit(movecommand, ";")[[1]], 
                                 ",|\\s+"), function(x) x[x != ""])
  movelist <- lapply(movecommand, function(x) {
    Where <- x[which(x %in% c("before", "after", "first", 
                              "last")):length(x)]
    ToMove <- setdiff(x, Where)
    list(ToMove, Where)
  })
  myVec <- invec
  for (i in seq_along(movelist)) {
    temp <- setdiff(myVec, movelist[[i]][[1]])
    A <- movelist[[i]][[2]][1]
    if (A %in% c("before", "after")) {
      ba <- movelist[[i]][[2]][2]
      if (A == "before") {
        after <- match(ba, temp) - 1
      }
      else if (A == "after") {
        after <- match(ba, temp)
      }
    }
    else if (A == "first") {
      after <- 0
    }
    else if (A == "last") {
      after <- length(myVec)
    }
    myVec <- append(temp, values = movelist[[i]][[1]], after = after)
  }
  
  df[,match(myVec, names(df))]
}

makeBarplot <- function(var_id_cohort, var_id_single, var_id){

        n_cohort <- df_cohort %>% select(tot_cases_n) %>% sum()#, outcome_death_n)
        var_cohort <- df_cohort[var_id_cohort] %>% sum(., na.rm = TRUE)#, outcome_death_n)
        
        n_single <- df_singlecases %>% nrow()
        
        var_single <- df_singlecases %>% filter(get(var_id_single) == TRUE) %>% nrow()
        
        n_all <- n_cohort + n_single
        var_all <- var_cohort + var_single
        
        bar_df_abs <- data.frame(x = c("cohort", "cohort", "single cases", "single cases", "all", "all"), col = c("total", var_id, "total", var_id, "total", var_id), vals = c(n_cohort, var_cohort, n_single, var_single, n_all, var_all) )
        
        bar_df_prct <- data.frame(x = c("cohort", "cohort", "single cases", "single cases", "all", "all"), col = c(paste0(var_id, " -"), paste0(var_id, " +"), paste0(var_id, " -"), paste0(var_id, " +"), paste0(var_id, " -"), paste0(var_id, " +")), vals = c(100-(var_cohort/n_cohort*100), var_cohort/n_cohort*100, 100-(var_single/n_single*100), var_single/n_single*100, 100-(var_all/n_all*100), var_all/n_all*100) )

        
        p_abs <- ggplot(bar_df_abs, aes(x = x, y =  vals, fill = col)) +
            geom_bar(stat = "identity", position = "dodge") +
            theme_bw() + 
            labs(title = paste0("Total cases vs ", var_id), subtitle = "Absolute numbers", x = "group", y = "n", col = "") +
  scale_fill_manual(values = wes_palette("Royal1"))
        
        
        p_prct <- ggplot(bar_df_prct, aes(x = x, y =  vals, fill = col)) +
            geom_bar(stat = "identity", position = "fill") +
            theme_bw() + 
            labs(title = paste0(var_id), subtitle = "Percent", x = "group", y = "%", col = "")  +
    scale_y_continuous(labels = scales::percent)+
  scale_fill_manual(values = wes_palette("Royal1"))
        
        ggarrange(p_abs, p_prct, legend = "bottom")
  
}

makeHeatmap_cohort <- function(param1, colname_single, exclude_single = NULL, plottitle, textsize = 3){
  var_cohort <- df_cohort %>% select(("cohort_id") | "tot_cases_n" | ( contains(param1) & contains("_n")))
  var_cohort$cohort_id <- paste0(var_cohort$cohort_id, " (n = ", as.character(var_cohort$tot_cases_n),")")
  var_cohort <- var_cohort %>% 
    gather(variable, value, 3:ncol(var_cohort)) %>% group_by(cohort_id, variable) %>% summarize(prct = value/tot_cases_n*100)
  var_cohort$variable <- sub("_n", "", var_cohort$variable)

if (!is.null(exclude_single)){
  var_single <- df_singlecases %>% select(-contains(exclude_single))
  var_single <- var_single %>% select(contains(colname_single))
} else
{
  var_single <- df_singlecases %>% select(contains(colname_single))
}

 #%>% select(-contains("any"))
cols <- sapply(var_single, is.logical)
var_single[,cols] <- lapply(var_single[,cols], as.numeric)
var_single <- colSums(var_single, na.rm = TRUE)
var_single <- var_single/nrow(df_singlecases)*100
var_single <- as.data.frame(var_single) %>% rownames_to_column()
var_single$cohort_id <- "single_cases"
colnames(var_single) <- c("variable", "prct", "cohort_id")


missing <- setdiff(var_single$variable, var_cohort$variable)
if (length(missing) != 0 ){
  missing_df <- data.frame(variable = missing, prct = NA, cohort_id = unique(var_cohort$cohort_id))
  var_cohort <- bind_rows(var_cohort, as_tibble(missing_df))
} else if (length(missing) == 0) {
  missing <- setdiff(var_cohort$variable, var_single$variable)
  if (length(missing) != 0){
  missing_df <- data.frame(variable = missing, prct = NA, cohort_id = unique(var_single$cohort_id))
  var_single <- bind_rows(var_single, as_tibble(missing_df))
  }
}

hm_cohort <- ggplot(var_cohort, aes(x = variable, y = cohort_id, fill = prct)) + 
    geom_tile() + theme_classic() +
    theme(axis.text.x=element_blank(), axis.ticks.x=element_blank(), axis.line=element_blank())+
   scale_fill_gradient(low = "yellow", high="red", na.value = "lightgray", limits = c(0,100)) +
    labs(x = "", y = "cohort", title =plottitle) +
    geom_text(aes(label=round(prct, 2)), size = textsize, color = "black")

hm_single <- ggplot(var_single, aes(x = variable, y = cohort_id, fill = prct)) + 
    geom_tile() +  theme_classic() +
    theme(axis.text.x=element_text(angle=90, hjust=1), axis.line=element_blank())+
    scale_fill_gradient(low = "yellow", high = "red", na.value = "lightgray", limits = c(0,100))+ labs(y = "cohort") +
    geom_text(aes(label=round(prct, 2)), size = textsize, color = "black") 

plot_grid(hm_cohort, hm_single, align = "v", nrow = 2, rel_heights = c(1/2, 1/2))
}

2 Data import and cleaning

2.1 Single cases

First, we import the single cases from the general excel sheet and transform the excel sheet so that variables are columns and rows are cases. Columns without any values are also removed.

The single cases from Pouletty (10.1136/annrheumdis-2020-217960) are excluded (as they are included in the cohorts).

2.1.1 Making summary statistics

In this section, data is summarized. For example, if there are any comorbidities present, a column “comorb_any” is added and annotated as TRUE. The same is done for COVID serology and symptoms of major organ (respiratory, cardiovascular etc).

If IgG, IgA, IgM or COVID serology is reported as positive, the column covid_sero_any is annotated as TRUE.

If PCR+, stool PCR+, IgG, IgA, IgM or COVID serology is reported as positive, the column covid_pos_any is annotated as TRUE.

If any respiratory symptoms, symp_resp_any is annotated as TRUE.

If any GI symptoms, symp_GI_any is annotated as TRUE.

If any neurological symptoms, symp_neuro_any is annotated as TRUE.

If any renal symptoms, symp_renal_any is annotated as TRUE.

If any cardiovascular symptoms, symp_cardiovasc_any is annotated as TRUE.

2.2 Cohorts

Afterwards, we do the same for the cohort sheet.

The papers by Grimaud et al. and Verdoni et al. are removed from the cohort dataframe, as most information is present in the single cases dataframe.

3 Descriptive statistics

3.1 General

Click on the any of the tabs above to see descriptive statistics for every variable

3.2 Single cases

How to read
Under “Variable type: logical”, the number of true/falses are depicted. E.g. at the top we can see that there are 95 number of rows (= 95 patients). Overweight has 79 missing values (17% is complete), which means that 95-79=16 patients have either “TRUE” or “FALSE” for overweight. Of these 16, 9 are marked as “TRUE” for overweight.

Download data as .csv on Github

Data summary
Name df_singlecases
Number of rows 95
Number of columns 156
_______________________
Column type frequency:
character 12
logical 90
numeric 54
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
doi 0 1.00 28 50 0 28 0
first_author 0 1.00 10 25 0 28 0
journal 0 1.00 6 62 0 22 0
prim_input 0 1.00 2 2 0 1 0
cross_check 6 0.94 3 20 0 2 0
patientID_pub 16 0.83 6 10 0 24 0
patientID_int 0 1.00 9 10 0 95 0
notes 91 0.04 12 66 0 4 0
sex 0 1.00 1 1 0 2 0
ethnicity 61 0.36 5 26 0 8 0
symp_fever_days 52 0.45 1 3 0 13 0
symp_cardiovasc_LVEF 40 0.58 2 3 0 25 0

Variable type: logical

skim_variable n_missing complete_rate mean count
overweight 58 0.39 0.35 FAL: 24, TRU: 13
comorb_any 79 0.17 1.00 TRU: 16
comorb_cardiovasc 70 0.26 0.04 FAL: 24, TRU: 1
comorb_resp 75 0.21 0.00 FAL: 20
comorb_resp_astma 87 0.08 1.00 TRU: 8
comorb_resp_hay 94 0.01 1.00 TRU: 1
comorb_diabetes 75 0.21 0.00 FAL: 20
comorb_renal 75 0.21 0.00 FAL: 20
comorb_malignancy 75 0.21 0.00 FAL: 20
comorb_immunodef 75 0.21 0.00 FAL: 20
comorb_CAH 94 0.01 1.00 TRU: 1
comorb_hypothyr 93 0.02 1.00 TRU: 2
comorb_alopecia 94 0.01 1.00 TRU: 1
comorb_crohn 94 0.01 1.00 TRU: 1
comorb_PFAPA 94 0.01 1.00 TRU: 1
comorb_NAFLD 94 0.01 1.00 TRU: 1
comorb_SBS 94 0.01 1.00 TRU: 1
covid_closecontact 69 0.27 0.58 TRU: 15, FAL: 11
covid_PCR_pos 3 0.97 0.42 FAL: 53, TRU: 39
covid_PCR_stool_pos 79 0.17 0.12 FAL: 14, TRU: 2
covid_pos_any 13 0.86 1.00 TRU: 82
covid_sero_any 33 0.65 1.00 TRU: 62
covid_sero_pos 80 0.16 1.00 TRU: 15
covid_IgA_pos 77 0.19 1.00 TRU: 18
covid_IgM_pos 78 0.18 0.41 FAL: 10, TRU: 7
covid_IgG_pos 45 0.53 0.94 TRU: 47, FAL: 3
symp_fever 0 1.00 1.00 TRU: 95
symp_resp_any 50 0.47 1.00 TRU: 45
symp_resp_NS 82 0.14 0.69 TRU: 9, FAL: 4
symp_resp_URT 80 0.16 0.73 TRU: 11, FAL: 4
symp_resp_dyspnea 66 0.31 0.76 TRU: 22, FAL: 7
symp_resp_pneumonia 48 0.49 0.70 TRU: 33, FAL: 14
symp_resp_failure 66 0.31 0.66 TRU: 19, FAL: 10
symp_resp_chestpain 88 0.07 0.29 FAL: 5, TRU: 2
symp_GI_any 15 0.84 1.00 TRU: 80
symp_GI_NS 85 0.11 1.00 TRU: 10
symp_GI_abdopain 28 0.71 0.97 TRU: 65, FAL: 2
symp_GI_vomiting 39 0.59 0.95 TRU: 53, FAL: 3
symp_GI_diarrh 45 0.53 0.84 TRU: 42, FAL: 8
symp_GI_colitis 91 0.04 1.00 TRU: 4
symp_neuro_any 68 0.28 1.00 TRU: 27
symp_neuro_headache 78 0.18 0.65 TRU: 11, FAL: 6
symp_neuro_meningitis 94 0.01 1.00 TRU: 1
symp_neuro_meningism 81 0.15 0.43 FAL: 8, TRU: 6
symp_neuro_asthenia 88 0.07 1.00 TRU: 7
symp_neuro_irritab 85 0.11 0.60 TRU: 6, FAL: 4
symp_dermato_NS 93 0.02 1.00 TRU: 2
symp_renal_any 80 0.16 0.87 TRU: 13, FAL: 2
symp_renal_AKI 80 0.16 0.87 TRU: 13, FAL: 2
symp_cardiovasc_any 8 0.92 1.00 TRU: 87
symp_cardiovasc_myocard 62 0.35 1.00 TRU: 33
symp_cardiovasc_pericard 59 0.38 0.61 TRU: 22, FAL: 14
symp_cardiovasc_cordilat 40 0.58 0.18 FAL: 45, TRU: 10
symp_cardiovasc_aneurysm 61 0.36 0.15 FAL: 29, TRU: 5
symp_cardiovasc_LV_30to55 38 0.60 0.47 FAL: 30, TRU: 27
symp_cardiovasc_LV_less30 68 0.28 0.22 FAL: 21, TRU: 6
symp_cardiovasc_shock 9 0.91 0.93 TRU: 80, FAL: 6
symp_cardiovasc_tachycard 26 0.73 1.00 TRU: 69
symp_cardiovasc_arrhyt 93 0.02 1.00 TRU: 2
kawasaki_complete 44 0.54 0.29 FAL: 36, TRU: 15
kawasaki_incomplete 42 0.56 0.45 FAL: 29, TRU: 24
kawasaki_fever 18 0.81 0.87 TRU: 67, FAL: 10
kawasaki_exanthema 28 0.71 0.69 TRU: 46, FAL: 21
kawasaki_extremity 68 0.28 0.74 TRU: 20, FAL: 7
kawasaki_mouth 50 0.47 0.53 TRU: 24, FAL: 21
kawasaki_cervical 53 0.44 0.24 FAL: 32, TRU: 10
kawasaki_conjunctivitis 32 0.66 0.67 TRU: 42, FAL: 21
symp_MAS 85 0.11 0.60 TRU: 6, FAL: 4
symp_effusion 76 0.20 0.68 TRU: 13, FAL: 6
admis_PICU_admis 52 0.45 0.98 TRU: 42, FAL: 1
critcare_NIV 39 0.59 0.61 TRU: 34, FAL: 22
critcare_MV 42 0.56 0.51 TRU: 27, FAL: 26
critcare_inotrop 18 0.81 0.79 TRU: 61, FAL: 16
critcare_ECMO 61 0.36 0.09 FAL: 31, TRU: 3
critcare_RRT 93 0.02 1.00 TRU: 2
rx_cortic 32 0.66 0.56 TRU: 35, FAL: 28
rx_aspirin_low 68 0.28 0.78 TRU: 21, FAL: 6
rx_aspirin_high 75 0.21 0.90 TRU: 18, FAL: 2
rx_aspirin_NS 88 0.07 0.71 TRU: 5, FAL: 2
rx_heparin 73 0.23 1.00 TRU: 22
rx_IVIg_once 17 0.82 0.96 TRU: 75, FAL: 3
rx_IVIg_multip 84 0.12 0.36 FAL: 7, TRU: 4
rx_anakinra 90 0.05 1.00 TRU: 5
rx_tocilizumab 75 0.21 1.00 TRU: 20
rx_infliximab 93 0.02 1.00 TRU: 2
rx_antibiotics 45 0.53 1.00 TRU: 50
rx_plasma 94 0.01 1.00 TRU: 1
rx_remdesivir 90 0.05 0.80 TRU: 4, FAL: 1
outcome_death 37 0.61 0.03 FAL: 56, TRU: 2
outcome_LVdysf 86 0.09 0.00 FAL: 9

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
date_of_publication 0 1.00 43987.13 12.83 43952.00 43979.00 43983.00 43998.50 44011.00 ▁▅▇▅▆
age 0 1.00 9.40 4.41 0.33 6.00 9.00 12.30 20.00 ▂▇▇▅▂
weight 71 0.25 46.77 26.61 5.60 25.00 44.50 64.50 105.00 ▇▇▇▅▂
BMI 60 0.37 21.59 6.40 12.00 16.50 20.00 25.45 36.70 ▆▇▆▂▃
symp_neuro_GCS 74 0.22 13.57 2.68 4.00 13.00 15.00 15.00 15.00 ▁▁▁▂▇
kawasaki_koyobashi 85 0.11 5.10 1.52 2.00 4.50 6.00 6.00 6.00 ▁▁▁▁▇
lab_Hb_lowest 77 0.19 10.39 1.94 7.00 8.67 10.95 11.75 13.10 ▅▂▁▇▅
lab_WBC_highest 70 0.26 18418.40 10027.21 2900.00 11800.00 15800.00 23110.00 40000.00 ▂▇▂▂▂
lab_lymphocytes_lowest 34 0.64 1082.43 1106.15 170.00 510.00 860.00 1150.00 7200.00 ▇▁▁▁▁
lab_neutrophils 61 0.36 14846.53 8563.92 1500.00 9392.50 11640.00 18780.50 36200.00 ▃▇▃▁▂
lab_platelets_NS 21 0.78 189500.00 94223.90 42000.00 121500.00 170000.00 234750.00 516000.00 ▆▇▃▁▁
lab_platelets_highest 89 0.06 583666.67 289892.85 250000.00 431500.00 551500.00 631000.00 1100000.00 ▇▃▇▁▃
lab_platelets_lowest 90 0.05 180600.00 90762.33 100000.00 111000.00 136000.00 260000.00 296000.00 ▇▁▁▁▅
lab_sodium 47 0.51 130.00 4.23 118.00 128.00 130.00 133.00 139.00 ▂▂▇▇▃
lab_ferritin_NS 59 0.38 1257.34 1196.76 199.00 535.00 906.50 1285.75 5440.00 ▇▂▁▁▁
lab_ferritin_admis 74 0.22 1843.70 2542.46 264.00 446.00 1089.00 1789.00 10170.00 ▇▁▁▁▁
lab_ferritin_peak 84 0.12 1348.12 1130.04 375.80 776.15 1096.20 1346.50 4488.00 ▇▃▁▁▁
lab_Ddim_NS 55 0.42 6124.88 5689.73 320.00 2230.00 3975.00 9155.00 24500.00 ▇▂▂▁▁
lab_Ddim_peak 86 0.09 8072.78 9005.15 508.00 2150.00 3300.00 11510.00 27760.00 ▇▂▃▁▂
lab_lactate 75 0.21 3.60 2.20 1.00 1.70 3.20 5.25 8.10 ▇▃▂▂▂
lab_fibrino 47 0.51 707.08 286.24 179.00 537.75 710.00 810.00 2140.00 ▃▇▁▁▁
lab_triglyc 83 0.13 348.00 275.10 121.00 186.00 231.50 383.75 987.00 ▇▂▁▁▁
lab_albumin_admis 40 0.58 26.17 7.11 17.30 20.75 24.00 31.50 43.00 ▇▅▂▂▂
lab_albumin_lowest 88 0.07 24.00 2.52 21.00 22.00 24.00 25.50 28.00 ▇▁▅▂▂
lab_albumin_NS 91 0.04 22.25 4.19 18.00 20.25 21.50 23.50 28.00 ▃▇▁▁▃
lab_creat 94 0.01 2.65 NA 2.65 2.65 2.65 2.65 2.65 ▁▁▇▁▁
lab_AST 75 0.21 90.15 64.00 28.00 47.00 70.00 103.50 239.00 ▇▃▂▁▂
lab_ALT_peak 49 0.48 71.17 110.14 6.00 23.25 48.00 78.75 733.00 ▇▁▁▁▁
lab_ALT_NS 90 0.05 71.20 11.56 52.00 69.00 76.00 79.00 80.00 ▂▁▁▂▇
lab_CK 84 0.12 81.36 61.96 16.00 43.00 76.00 86.50 247.00 ▇▆▂▁▂
lab_LDH 76 0.20 541.89 279.64 283.00 307.00 408.00 761.00 1059.00 ▇▁▂▂▂
lab_troponin_admis 26 0.73 991.29 3442.01 0.00 47.00 140.00 470.00 27360.00 ▇▁▁▁▁
lab_troponin_max 73 0.23 993.83 1564.33 4.00 132.00 387.00 932.90 6170.00 ▇▂▁▁▁
lab_NTproBNP 71 0.25 8518.34 11115.95 72.44 782.00 2514.50 13273.25 35000.00 ▇▂▁▁▁
lab_BNP_admis 57 0.40 4329.53 5458.54 0.00 408.50 2265.00 5847.50 19013.00 ▇▁▁▁▁
lab_BNP_max 84 0.12 3689.04 5271.04 517.70 957.80 1718.60 3177.00 18606.50 ▇▁▁▁▁
lab_CRP_admis 16 0.83 254.03 116.23 9.00 168.00 249.90 318.00 556.00 ▂▇▇▂▂
lab_CRP_NS 84 0.12 287.04 131.78 7.40 227.50 328.00 369.50 456.00 ▂▂▃▇▆
lab_CRP_peak 82 0.14 277.29 93.91 164.00 191.00 250.00 357.00 425.00 ▇▃▂▅▃
lab_PCT_admis 40 0.58 46.54 88.86 0.11 5.78 15.20 41.39 448.00 ▇▁▁▁▁
lab_PCT_peak 84 0.12 48.11 57.08 2.45 15.12 28.40 56.37 200.00 ▇▂▁▁▁
lab_PCT_NS 88 0.07 31.51 39.75 0.13 3.50 14.80 49.30 100.00 ▇▂▁▂▂
lab_ESR 75 0.21 70.20 26.66 11.00 55.50 65.00 86.00 118.00 ▁▂▇▂▃
lab_IL6 69 0.27 278.33 278.76 11.60 109.30 235.00 335.00 1449.00 ▇▃▁▁▁
lab_IL8 75 0.21 53.59 41.98 9.40 27.25 41.40 54.42 149.00 ▇▇▁▁▃
lab_TNF 75 0.21 40.52 21.99 10.70 23.08 37.25 52.92 97.80 ▇▂▅▂▁
lab_IL1 75 0.21 0.62 0.51 0.00 0.30 0.40 0.90 1.60 ▇▇▃▁▃
lab_IL2 94 0.01 3157.00 NA 3157.00 3157.00 3157.00 3157.00 3157.00 ▁▁▇▁▁
admis_hosp_days 68 0.28 9.19 3.62 2.00 7.00 8.00 12.00 17.00 ▂▇▃▅▂
admis_ICU_days 71 0.25 5.25 2.94 0.00 3.75 4.00 8.00 11.00 ▂▇▂▅▂
lab_PELOD2 75 0.21 12.50 4.39 10.00 10.00 10.50 11.00 22.00 ▇▁▁▁▂
critcare_NIV_days 93 0.02 3.00 2.83 1.00 2.00 3.00 4.00 5.00 ▇▁▁▁▇
critcare_MV_days 93 0.02 4.50 0.71 4.00 4.25 4.50 4.75 5.00 ▇▁▁▁▇
critcare_inotrop_days 91 0.04 4.38 3.90 1.50 1.88 3.00 5.50 10.00 ▇▃▁▁▃

3.3 Cohorts

How to read
The sum column equals the sum of all individuals, e.g. sum(tot_cases_n) means that there are 592 patients in total in the cohorts; sum(outcome_death_n) means that 9 patients died.

The “Prct_total” column is the percentage of e.g. death (9/592). Only makes sense where n is reported e.g. therapy (not for lab values).

Data summary
Name df_cohort
Number of rows 15
Number of columns 341
_______________________
Column type frequency:
character 8
numeric 333
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
doi 0 1.0 21 49 0 12 0
first_author 0 1.0 13 19 0 12 0
journal 0 1.0 3 16 0 9 0
cohort_id 0 1.0 6 20 0 15 0
cohort_type 0 1.0 5 5 0 1 0
prim_input 0 1.0 2 2 0 1 0
cross_check 0 1.0 3 3 0 1 0
notes 6 0.6 4 51 0 9 0

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist sum Prct_total
date_of_publication 0 1.00 43995.80 12.66 43968.0 43990.00 43994.00 44009.00 44011.0 ▂▃▆▅▇ 659937.0 111475.84
tot_cases_n 0 1.00 39.47 42.80 7.0 19.00 33.00 38.50 186.0 ▇▁▁▁▁ 592.0 100.00
sex_m 0 1.00 23.13 26.91 4.0 10.00 18.00 20.00 115.0 ▇▁▁▁▁ 347.0 58.61
sex_f 0 1.00 16.33 16.47 3.0 8.50 12.00 18.50 71.0 ▇▃▁▁▁ 245.0 41.39
age_med_yrs 3 0.80 8.82 1.82 5.0 7.97 8.70 10.00 12.0 ▁▁▇▂▂ 105.9 17.89
age_Q1_yrs 8 0.47 5.49 2.39 2.0 4.40 5.70 6.20 9.5 ▃▁▇▁▂ 38.4 6.49
age_Q3_yrs 8 0.47 12.69 1.79 10.0 11.85 12.60 13.50 15.5 ▂▂▇▂▂ 88.8 15.00
age_min_yrs 10 0.33 1.66 1.44 0.0 0.60 1.80 2.20 3.7 ▇▁▇▁▃ 8.3 1.40
age_max_yrs 10 0.33 17.92 1.93 16.0 16.60 17.00 20.00 20.0 ▇▃▁▁▇ 89.6 15.14
age_n_under1 13 0.13 6.50 9.19 0.0 3.25 6.50 9.75 13.0 ▇▁▁▁▇ 13.0 2.20
age_n_1to4 14 0.07 53.00 NA 53.0 53.00 53.00 53.00 53.0 ▁▁▇▁▁ 53.0 8.95
age_n_1to5 14 0.07 1.00 NA 1.0 1.00 1.00 1.00 1.0 ▁▁▇▁▁ 1.0 0.17
age_n_5to9 14 0.07 46.00 NA 46.0 46.00 46.00 46.00 46.0 ▁▁▇▁▁ 46.0 7.77
age_n_6to10 14 0.07 15.00 NA 15.0 15.00 15.00 15.00 15.0 ▁▁▇▁▁ 15.0 2.53
age_n_10to14 14 0.07 45.00 NA 45.0 45.00 45.00 45.00 45.0 ▁▁▇▁▁ 45.0 7.60
age_n_11to16 14 0.07 19.00 NA 19.0 19.00 19.00 19.00 19.0 ▁▁▇▁▁ 19.0 3.21
age_n_0to5 13 0.13 17.50 19.09 4.0 10.75 17.50 24.25 31.0 ▇▁▁▁▇ 35.0 5.91
age_n_6to12 14 0.07 42.00 NA 42.0 42.00 42.00 42.00 42.0 ▁▁▇▁▁ 42.0 7.09
age_n_13to20 13 0.13 35.50 13.44 26.0 30.75 35.50 40.25 45.0 ▇▁▁▁▇ 71.0 11.99
age_n_15to20 14 0.07 29.00 NA 29.0 29.00 29.00 29.00 29.0 ▁▁▇▁▁ 29.0 4.90
race_n_white 4 0.73 11.82 9.16 0.0 7.50 12.00 12.50 35.0 ▃▇▁▁▁ 130.0 21.96
race_n_black 4 0.73 14.82 12.15 4.0 7.00 12.00 17.50 46.0 ▇▂▂▁▁ 163.0 27.53
race_n_asian 6 0.60 4.11 5.51 0.0 1.00 2.00 4.00 18.0 ▇▂▁▁▁ 37.0 6.25
race_n_other 5 0.67 7.60 7.66 1.0 3.00 5.00 8.50 26.0 ▇▂▁▁▁ 76.0 12.84
race_n_unknown 13 0.13 22.50 26.16 4.0 13.25 22.50 31.75 41.0 ▇▁▁▁▇ 45.0 7.60
ethn_n_hisp 13 0.13 33.00 33.94 9.0 21.00 33.00 45.00 57.0 ▇▁▁▁▇ 66.0 11.15
eth_n_nonhisp 14 0.07 24.00 NA 24.0 24.00 24.00 24.00 24.0 ▁▁▇▁▁ 24.0 4.05
weight_med_kg 14 0.07 33.40 NA 33.4 33.40 33.40 33.40 33.4 ▁▁▇▁▁ 33.4 5.64
bmi_med 13 0.13 18.02 0.83 17.4 17.72 18.02 18.31 18.6 ▇▁▁▁▇ 36.0 6.09
bmi_Q1 14 0.07 15.90 NA 15.9 15.90 15.90 15.90 15.9 ▁▁▇▁▁ 15.9 2.69
bmi_Q3 14 0.07 22.90 NA 22.9 22.90 22.90 22.90 22.9 ▁▁▇▁▁ 22.9 3.87
n_overweight 5 0.67 11.90 12.47 2.0 6.00 9.00 12.50 45.0 ▇▃▁▁▁ 119.0 20.10
comorb_n_cardiovasc 12 0.20 2.33 2.52 0.0 1.00 2.00 3.50 5.0 ▇▇▁▁▇ 7.0 1.18
comorb_n_resp 14 0.07 33.00 NA 33.0 33.00 33.00 33.00 33.0 ▁▁▇▁▁ 33.0 5.57
comorb_n_astma 8 0.47 3.00 1.73 0.0 2.50 3.00 4.00 5.0 ▂▂▇▁▅ 21.0 3.55
comorb_n_chronlungdz 12 0.20 4.67 2.52 2.0 3.50 5.00 6.00 7.0 ▇▁▇▁▇ 14.0 2.36
comorb_n_immunodef 14 0.07 10.00 NA 10.0 10.00 10.00 10.00 10.0 ▁▁▇▁▁ 10.0 1.69
comorb_n_NS 14 0.07 3.00 NA 3.0 3.00 3.00 3.00 3.0 ▁▁▇▁▁ 3.0 0.51
comorb_n_lupus 14 0.07 1.00 NA 1.0 1.00 1.00 1.00 1.0 ▁▁▇▁▁ 1.0 0.17
comorb_n_neurodis 14 0.07 1.00 NA 1.0 1.00 1.00 1.00 1.0 ▁▁▇▁▁ 1.0 0.17
comorb_n_sickle 14 0.07 1.00 NA 1.0 1.00 1.00 1.00 1.0 ▁▁▇▁▁ 1.0 0.17
comorb_n_alopecia 14 0.07 1.00 NA 1.0 1.00 1.00 1.00 1.0 ▁▁▇▁▁ 1.0 0.17
covid_n_closecontact 7 0.53 14.00 16.87 3.0 6.00 9.00 11.50 55.0 ▇▁▁▁▁ 112.0 18.92
covid_n_PCR_pos 0 1.00 14.20 17.15 1.0 6.50 11.00 15.00 73.0 ▇▁▁▁▁ 213.0 35.98
covid_n_PCR_neg 2 0.87 25.69 27.29 2.0 10.00 22.00 25.00 108.0 ▇▃▁▁▁ 334.0 56.42
covid_n_PCR_stool_pos 10 0.33 0.80 0.84 0.0 0.00 1.00 1.00 2.0 ▇▁▇▁▃ 4.0 0.68
covid_n_PCR_stool_neg 10 0.33 10.60 13.28 0.0 3.00 5.00 12.00 33.0 ▇▂▁▁▂ 53.0 8.95
covid_n_sero_pos 10 0.33 32.80 30.66 9.0 12.00 27.00 31.00 85.0 ▇▇▁▁▃ 164.0 27.70
covid_n_sero_neg 10 0.33 11.80 19.42 0.0 0.00 5.00 8.00 46.0 ▇▁▁▁▂ 59.0 9.97
covid_n_sero_any 14 0.07 30.00 NA 30.0 30.00 30.00 30.00 30.0 ▁▁▇▁▁ 30.0 5.07
covid_n_IgA_pos 14 0.07 25.00 NA 25.0 25.00 25.00 25.00 25.0 ▁▁▇▁▁ 25.0 4.22
covid_n_IgA_neg 14 0.07 10.00 NA 10.0 10.00 10.00 10.00 10.0 ▁▁▇▁▁ 10.0 1.69
covid_n_IgM_pos 13 0.13 12.50 14.85 2.0 7.25 12.50 17.75 23.0 ▇▁▁▁▇ 25.0 4.22
covid_n_IgM_neg 13 0.13 30.00 4.24 27.0 28.50 30.00 31.50 33.0 ▇▁▁▁▇ 60.0 10.14
covid_n_IgG_pos 5 0.67 22.50 11.83 3.0 19.00 24.50 29.50 40.0 ▃▁▇▃▃ 225.0 38.01
covid_n_IgG_neg 5 0.67 4.30 8.38 0.0 0.00 1.00 4.75 27.0 ▇▂▁▁▁ 43.0 7.26
symp_fever_n 1 0.93 40.50 44.15 7.0 19.25 32.00 40.25 186.0 ▇▂▁▁▁ 567.0 95.78
symp_fever_days_min 13 0.13 2.00 1.41 1.0 1.50 2.00 2.50 3.0 ▇▁▁▁▇ 4.0 0.68
symp_fever_days_med 10 0.33 6.60 1.82 5.0 5.00 6.00 8.00 9.0 ▇▃▁▃▃ 33.0 5.57
symp_fever_days_max 13 0.13 15.50 4.95 12.0 13.75 15.50 17.25 19.0 ▇▁▁▁▇ 31.0 5.24
symp_fever_days_Q1 12 0.20 7.33 2.08 5.0 6.50 8.00 8.50 9.0 ▇▁▁▇▇ 22.0 3.72
symp_fever_days_Q3 12 0.20 10.67 2.31 8.0 10.00 12.00 12.00 12.0 ▃▁▁▁▇ 32.0 5.41
symp_resp_any_n 7 0.53 25.38 43.33 1.0 5.50 11.50 18.50 131.0 ▇▁▁▁▁ 203.0 34.29
symp_resp_URT_n 10 0.33 9.60 3.91 6.0 6.00 9.00 12.00 15.0 ▇▃▁▃▃ 48.0 8.11
symp_resp_dyspnea_n 10 0.33 10.60 7.30 5.0 6.00 8.00 11.00 23.0 ▇▂▁▁▂ 53.0 8.95
symp_resp_pneumonia_n 11 0.27 10.25 4.99 3.0 9.00 12.00 13.25 14.0 ▃▁▁▃▇ 41.0 6.93
symp_resp_chestpain_n 11 0.27 4.25 2.75 1.0 2.50 4.50 6.25 7.0 ▃▃▁▁▇ 17.0 2.87
symp_resp_failure_n 9 0.40 22.17 42.99 0.0 1.50 3.00 13.50 109.0 ▇▁▁▁▂ 133.0 22.47
symp_GI_any_n 2 0.87 33.85 42.42 6.0 15.00 23.00 32.00 171.0 ▇▁▁▁▁ 440.0 74.32
symp_GI_abdopain_n 9 0.40 24.17 8.01 13.0 18.75 25.00 30.50 33.0 ▃▇▁▃▇ 145.0 24.49
symp_GI_vomiting_n 8 0.47 21.57 4.28 16.0 18.00 23.00 25.00 26.0 ▅▂▁▂▇ 151.0 25.51
symp_GI_diarrh_n 8 0.47 19.00 6.06 13.0 14.50 18.00 21.50 30.0 ▇▂▅▁▂ 133.0 22.47
symp_GI_colitis_n 14 0.07 3.00 NA 3.0 3.00 3.00 3.00 3.0 ▁▁▇▁▁ 3.0 0.51
symp_neuro_any_n 4 0.73 9.64 5.16 4.0 4.50 10.00 12.50 19.0 ▇▃▆▂▂ 106.0 17.91
symp_neuro_encefalo_n 14 0.07 4.00 NA 4.0 4.00 4.00 4.00 4.0 ▁▁▇▁▁ 4.0 0.68
symp_neuro_asthenia_n 10 0.33 8.20 15.06 0.0 1.00 1.00 4.00 35.0 ▇▁▁▁▂ 41.0 6.93
symp_neuro_headache_n 11 0.27 11.00 5.23 4.0 8.50 12.50 15.00 15.0 ▃▁▃▁▇ 44.0 7.43
symp_neuro_irritab_n 14 0.07 12.00 NA 12.0 12.00 12.00 12.00 12.0 ▁▁▇▁▁ 12.0 2.03
symp_dermato_any_n 12 0.20 20.33 7.23 12.0 18.00 24.00 24.50 25.0 ▃▁▁▁▇ 61.0 10.30
symp_renal_any_n 14 0.07 15.00 NA 15.0 15.00 15.00 15.00 15.0 ▁▁▇▁▁ 15.0 2.53
symp_renal_AKI_n 8 0.47 9.00 7.23 3.0 3.50 7.00 11.50 23.0 ▇▂▂▁▂ 63.0 10.64
symp_cardiovasc_any_n 13 0.13 85.50 89.80 22.0 53.75 85.50 117.25 149.0 ▇▁▁▁▇ 171.0 28.89
symp_cardiovasc_myocard_n 8 0.47 12.86 7.15 1.0 9.00 15.00 17.50 21.0 ▃▃▃▇▇ 90.0 15.20
symp_cardiovasc_pericard_n 7 0.53 9.25 8.14 1.0 3.00 8.00 11.25 26.0 ▇▇▂▁▂ 74.0 12.50
symp_cardiovasc_cordilat_n 5 0.67 5.20 4.29 0.0 2.25 5.50 6.00 15.0 ▇▇▂▁▂ 52.0 8.78
symp_cardiovasc_aneurysm_n 6 0.60 2.67 2.74 0.0 1.00 1.00 4.00 8.0 ▇▁▃▂▂ 24.0 4.05
symp_cardiovasc_LV_less30_n 8 0.47 3.71 4.19 0.0 0.50 2.00 6.50 10.0 ▇▂▁▁▃ 26.0 4.39
symp_cardiovasc_LV_30to55_n 9 0.40 23.67 19.17 9.0 12.50 18.00 23.50 61.0 ▇▂▁▁▂ 142.0 23.99
symp_cardiovasc_LVEF_med 10 0.33 44.72 6.95 34.0 42.00 46.60 50.00 51.0 ▃▁▃▃▇ 223.6 37.77
symp_cardiovasc_LVEF_Q1 14 0.07 39.50 NA 39.5 39.50 39.50 39.50 39.5 ▁▁▇▁▁ 39.5 6.67
symp_cardiovasc_LVEF_Q3 14 0.07 52.80 NA 52.8 52.80 52.80 52.80 52.8 ▁▁▇▁▁ 52.8 8.92
symp_cardiovasc_LVEF_min 13 0.13 6.50 4.95 3.0 4.75 6.50 8.25 10.0 ▇▁▁▁▇ 13.0 2.20
symp_cardiovasc_LVEF_max 13 0.13 50.00 9.90 43.0 46.50 50.00 53.50 57.0 ▇▁▁▁▇ 100.0 16.89
symp_cardiovasc_shock_n 2 0.87 14.62 10.06 1.0 5.00 13.00 22.00 29.0 ▇▃▂▆▆ 190.0 32.09
symp_cardiovasc_tachycard_n 10 0.33 21.40 15.81 4.0 7.00 26.00 28.00 42.0 ▇▁▃▃▃ 107.0 18.07
symp_cardiovasc_arrhyt_n 11 0.27 7.50 9.75 1.0 2.50 3.50 8.50 22.0 ▇▁▁▁▂ 30.0 5.07
symp_cardiovasc_thrombo_n 13 0.13 0.00 0.00 0.0 0.00 0.00 0.00 0.0 ▁▁▇▁▁ 0.0 0.00
kawasaki_complete_n 7 0.53 11.88 12.21 0.0 5.50 7.50 13.50 38.0 ▇▃▂▁▂ 95.0 16.05
kawasaki_incomplete_n 11 0.27 14.75 14.31 5.0 7.25 9.00 16.50 36.0 ▇▁▁▁▂ 59.0 9.97
kawasaki_comp_or_incomp_n 11 0.27 27.50 31.67 3.0 12.00 16.50 32.00 74.0 ▇▃▁▁▃ 110.0 18.58
kawasaki_fever_n 11 0.27 45.50 58.41 7.0 8.50 22.00 59.00 131.0 ▇▃▁▁▃ 182.0 30.74
kawasaki_exanthema_n 2 0.87 24.46 26.94 6.0 12.00 16.00 25.00 110.0 ▇▂▁▁▁ 318.0 53.72
kawasaki_mouth_n 3 0.80 17.50 19.95 4.0 7.75 12.00 17.50 78.0 ▇▂▁▁▁ 210.0 35.47
kawasaki_extremity_n 6 0.60 12.33 21.46 1.0 3.00 6.00 9.00 69.0 ▇▁▁▁▁ 111.0 18.75
kawasaki_cervical_n 5 0.67 7.80 7.11 0.0 3.00 5.00 11.25 21.0 ▇▂▃▁▃ 78.0 13.18
kawasaki_conjunctivitis_n 2 0.87 23.23 25.17 6.0 11.00 17.00 23.00 103.0 ▇▂▁▁▁ 302.0 51.01
symp_effusion_n 13 0.13 8.50 4.95 5.0 6.75 8.50 10.25 12.0 ▇▁▁▁▇ 17.0 2.87
symp_arthritis_n 12 0.20 1.67 2.08 0.0 0.50 1.00 2.50 4.0 ▇▇▁▁▇ 5.0 0.84
symp_mas 13 0.13 0.00 0.00 0.0 0.00 0.00 0.00 0.0 ▁▁▇▁▁ 0.0 0.00
lab_Hb_NS_n 10 0.33 37.80 13.92 21.0 33.00 33.00 44.00 58.0 ▃▇▁▃▃ 189.0 31.93
lab_Hb_NS_med 10 0.33 10.30 1.30 8.6 9.20 11.20 11.20 11.3 ▂▂▁▁▇ 51.5 8.70
lab_Hb_NS_Q1 12 0.20 9.45 1.10 8.3 8.93 9.55 10.03 10.5 ▇▁▇▁▇ 28.4 4.79
lab_Hb_NS_Q3 12 0.20 11.60 1.15 10.3 11.15 12.00 12.25 12.5 ▇▁▁▇▇ 34.8 5.88
lab_Hb_baseline_n 14 0.07 17.00 NA 17.0 17.00 17.00 17.00 17.0 ▁▁▇▁▁ 17.0 2.87
lab_Hb_baseline_med 14 0.07 11.20 NA 11.2 11.20 11.20 11.20 11.2 ▁▁▇▁▁ 11.2 1.89
lab_WBC_NS_n 6 0.60 30.56 16.19 7.0 21.00 33.00 35.00 58.0 ▃▂▇▂▂ 275.0 46.45
lab_WBC_NS_med 3 0.80 12151.67 3022.43 9100.0 9695.00 11100.00 13525.00 17400.0 ▇▇▂▁▆ 145820.0 24631.76
lab_WBC_NS_Q1 5 0.67 9334.00 2129.50 6400.0 7550.00 9075.00 11400.00 12000.0 ▇▅▂▂▇ 93340.0 15766.89
lab_WBC_NS_Q3 5 0.67 17333.00 5852.26 11900.0 13775.00 14600.00 20800.00 30000.0 ▇▁▁▁▁ 173330.0 29278.72
lab_WBC_baseline_n 14 0.07 17.00 NA 17.0 17.00 17.00 17.00 17.0 ▁▁▇▁▁ 17.0 2.87
lab_WBC_baseline_med 14 0.07 14000.00 NA 14000.0 14000.00 14000.00 14000.00 14000.0 ▁▁▇▁▁ 14000.0 2364.86
lab_neutro_NS_n 9 0.40 27.33 19.15 7.0 12.00 27.50 34.75 58.0 ▇▃▇▁▃ 164.0 27.70
lab_neutro_NS_med 9 0.40 11333.33 2211.49 8000.0 10100.00 11700.00 13000.00 13600.0 ▂▂▂▁▇ 68000.0 11486.49
lab_neutro_NS_Q1 10 0.33 8520.00 1425.48 6400.0 8000.00 8600.00 9600.00 10000.0 ▃▁▃▃▇ 42600.0 7195.95
lab_neutro_NS_Q3 10 0.33 14260.00 4497.55 9600.0 10700.00 13000.00 19000.00 19000.0 ▇▃▁▁▇ 71300.0 12043.92
lab_lympho_NS_n 8 0.47 28.00 17.56 7.0 15.00 33.00 34.00 58.0 ▅▂▇▁▂ 196.0 33.11
lab_lympho_NS_med 8 0.47 1047.14 273.66 800.0 865.00 1000.00 1100.00 1600.0 ▇▇▁▁▂ 7330.0 1238.18
lab_lympho_NS_Q1 9 0.40 676.67 195.82 490.0 525.00 635.00 767.50 1000.0 ▇▇▁▃▃ 4060.0 685.81
lab_lympho_NS_Q3 9 0.40 1390.00 199.10 1120.0 1300.00 1360.00 1480.00 1700.0 ▃▇▃▃▃ 8340.0 1408.78
lab_lympho_baseline_n 14 0.07 17.00 NA 17.0 17.00 17.00 17.00 17.0 ▁▁▇▁▁ 17.0 2.87
lab_lympho_baseline_med 14 0.07 1212.10 NA 1212.1 1212.10 1212.10 1212.10 1212.1 ▁▁▇▁▁ 1212.1 204.75
lab_platelet_NS_n 4 0.73 30.45 14.79 7.0 23.50 33.00 37.50 58.0 ▃▃▇▃▂ 335.0 56.59
lab_platelet_NS_med 4 0.73 199000.00 101826.32 125000.0 157000.00 176000.00 189500.00 499000.0 ▇▁▁▁▁ 2189000.0 369763.51
lab_platelet_NS_Q1 6 0.60 124055.56 22705.24 100000.0 104000.00 130000.00 135000.00 170000.0 ▇▁▇▁▂ 1116500.0 188597.97
lab_platelet_NS_Q3 6 0.60 234333.33 33797.19 200000.0 210000.00 224000.00 240000.00 297000.0 ▇▇▂▁▅ 2109000.0 356250.00
lab_platelet_baseline_n 14 0.07 17.00 NA 17.0 17.00 17.00 17.00 17.0 ▁▁▇▁▁ 17.0 2.87
lab_platelet_baseline_med 14 0.07 237000.00 NA 237000.0 237000.00 237000.00 237000.00 237000.0 ▁▁▇▁▁ 237000.0 40033.78
lab_platelet_lowest_n 14 0.07 185.00 NA 185.0 185.00 185.00 185.00 185.0 ▁▁▇▁▁ 185.0 31.25
lab_platelet_lowest_med 14 0.07 133000.00 NA 133000.0 133000.00 133000.00 133000.00 133000.0 ▁▁▇▁▁ 133000.0 22466.22
lab_platelet_lowest_Q1 14 0.07 88000.00 NA 88000.0 88000.00 88000.00 88000.00 88000.0 ▁▁▇▁▁ 88000.0 14864.86
lab_platelet_lowest_Q3 14 0.07 235000.00 NA 235000.0 235000.00 235000.00 235000.00 235000.0 ▁▁▇▁▁ 235000.0 39695.95
lab_sodium_NS_n 10 0.33 20.60 12.52 7.0 9.00 21.00 33.00 33.0 ▇▁▃▁▇ 103.0 17.40
lab_sodium_NS_med 10 0.33 131.20 3.42 127.0 130.00 130.00 133.00 136.0 ▃▇▁▃▃ 656.0 110.81
lab_sodium_NS_Q1 11 0.27 130.50 3.42 127.0 128.50 130.00 132.00 135.0 ▇▇▇▁▇ 522.0 88.18
lab_sodium_NS_Q3 11 0.27 135.50 2.89 132.0 134.25 135.50 136.75 139.0 ▃▁▇▁▃ 542.0 91.55
lab_sodium_baseline_n 14 0.07 17.00 NA 17.0 17.00 17.00 17.00 17.0 ▁▁▇▁▁ 17.0 2.87
lab_sodium_baseline_med 14 0.07 133.10 NA 133.1 133.10 133.10 133.10 133.1 ▁▁▇▁▁ 133.1 22.48
lab_ferritin_NS_n 6 0.60 28.67 15.42 7.0 23.00 33.00 35.00 58.0 ▃▃▇▁▂ 258.0 43.58
lab_ferritin_NS_med 6 0.60 674.33 423.04 295.0 540.00 610.00 631.00 1760.0 ▇▇▁▁▂ 6069.0 1025.17
lab_ferritin_NS_Q1 6 0.60 446.44 472.82 165.0 293.00 313.00 359.00 1693.0 ▇▁▁▁▁ 4018.0 678.72
lab_ferritin_NS_Q3 6 0.60 1110.56 568.27 536.0 880.00 954.00 1192.00 2500.0 ▅▇▁▁▂ 9995.0 1688.34
lab_ferritin_baseline_n 14 0.07 17.00 NA 17.0 17.00 17.00 17.00 17.0 ▁▁▇▁▁ 17.0 2.87
lab_ferritin_baseline_med 14 0.07 647.90 NA 647.9 647.90 647.90 647.90 647.9 ▁▁▇▁▁ 647.9 109.44
lab_ferritin_peak_n 13 0.13 89.00 104.65 15.0 52.00 89.00 126.00 163.0 ▇▁▁▁▇ 178.0 30.07
lab_ferritin_peak_med 13 0.13 598.50 57.28 558.0 578.25 598.50 618.75 639.0 ▇▁▁▁▇ 1197.0 202.20
lab_ferritin_peak_Q1 13 0.13 348.35 22.13 332.7 340.52 348.35 356.18 364.0 ▇▁▁▁▇ 696.7 117.69
lab_ferritin_peak_Q3 13 0.13 1251.60 103.80 1178.2 1214.90 1251.60 1288.30 1325.0 ▇▁▁▁▇ 2503.2 422.84
lab_ALT_NS_n 10 0.33 37.80 13.92 21.0 33.00 33.00 44.00 58.0 ▃▇▁▃▃ 189.0 31.93
lab_ALT_NS_median 10 0.33 42.10 16.90 24.5 36.00 38.00 42.00 70.0 ▂▇▁▁▂ 210.5 35.56
lab_ALT_NS_Q1 12 0.20 28.00 2.00 26.0 27.00 28.00 29.00 30.0 ▇▁▇▁▇ 84.0 14.19
lab_ALT_NS_Q3 12 0.20 70.67 21.78 53.0 58.50 64.00 79.50 95.0 ▇▇▁▁▇ 212.0 35.81
lab_ALT_baseline_n 14 0.07 17.00 NA 17.0 17.00 17.00 17.00 17.0 ▁▁▇▁▁ 17.0 2.87
lab_ALT_baseline_med 14 0.07 49.60 NA 49.6 49.60 49.60 49.60 49.6 ▁▁▇▁▁ 49.6 8.38
lab_AST_NS_n 12 0.20 36.67 6.35 33.0 33.00 33.00 38.50 44.0 ▇▁▁▁▃ 110.0 18.58
lab_AST_NS_med 12 0.20 44.33 11.93 31.0 39.50 48.00 51.00 54.0 ▇▁▁▇▇ 133.0 22.47
lab_AST_NS_Q1 13 0.13 31.50 6.36 27.0 29.25 31.50 33.75 36.0 ▇▁▁▁▇ 63.0 10.64
lab_AST_NS_Q3 13 0.13 72.50 4.95 69.0 70.75 72.50 74.25 76.0 ▇▁▁▁▇ 145.0 24.49
lab_AST_baseline_n 14 0.07 17.00 NA 17.0 17.00 17.00 17.00 17.0 ▁▁▇▁▁ 17.0 2.87
lab_AST_baseline_med 14 0.07 51.50 NA 51.5 51.50 51.50 51.50 51.5 ▁▁▇▁▁ 51.5 8.70
lab_albumin_NS_n 5 0.67 29.90 15.66 7.0 21.75 31.00 39.00 58.0 ▅▅▇▅▂ 299.0 50.51
lab_albumin_NS_med 5 0.67 28.55 6.57 18.5 22.50 31.00 33.50 37.0 ▇▂▁▇▇ 285.5 48.23
lab_albumin_NS_Q1 7 0.53 24.12 3.68 18.0 21.75 25.00 26.00 30.0 ▃▇▇▇▃ 193.0 32.60
lab_albumin_NS_Q3 7 0.53 32.00 7.25 20.0 26.25 35.50 37.25 39.0 ▂▃▁▂▇ 256.0 43.24
lab_albumin_low_n 14 0.07 178.00 NA 178.0 178.00 178.00 178.00 178.0 ▁▁▇▁▁ 178.0 30.07
lab_albumin_low_med 14 0.07 25.00 NA 25.0 25.00 25.00 25.00 25.0 ▁▁▇▁▁ 25.0 4.22
lab_albumin_low_Q1 14 0.07 20.00 NA 20.0 20.00 20.00 20.00 20.0 ▁▁▇▁▁ 20.0 3.38
lab_albumin_low_Q3 14 0.07 29.00 NA 29.0 29.00 29.00 29.00 29.0 ▁▁▇▁▁ 29.0 4.90
lab_LDH_baseline_n 13 0.13 18.00 1.41 17.0 17.50 18.00 18.50 19.0 ▇▁▁▁▇ 36.0 6.08
lab_LDH_baseline_med 13 0.13 254.90 152.59 147.0 200.95 254.90 308.85 362.8 ▇▁▁▁▇ 509.8 86.11
lab_LDH_baseline_Q1 14 0.07 110.00 NA 110.0 110.00 110.00 110.00 110.0 ▁▁▇▁▁ 110.0 18.58
lab_LDH_baseline_Q3 14 0.07 510.00 NA 510.0 510.00 510.00 510.00 510.0 ▁▁▇▁▁ 510.0 86.15
lab_LDH_NS_n 11 0.27 25.50 7.55 19.0 19.00 25.00 31.50 33.0 ▇▁▁▁▇ 102.0 17.23
lab_LDH_NS_med 11 0.27 315.25 18.46 288.0 312.00 322.50 325.75 328.0 ▃▁▁▃▇ 1261.0 213.01
lab_LDH_NS_Q1 11 0.27 268.75 11.79 256.0 261.25 268.00 275.50 283.0 ▇▇▁▇▇ 1075.0 181.59
lab_LDH_NS_Q3 11 0.27 413.00 51.48 342.0 399.75 422.50 435.75 465.0 ▃▁▁▇▃ 1652.0 279.05
lab_CK_baseline_n 14 0.07 19.00 NA 19.0 19.00 19.00 19.00 19.0 ▁▁▇▁▁ 19.0 3.21
lab_CK_baseline_med 14 0.07 147.00 NA 147.0 147.00 147.00 147.00 147.0 ▁▁▇▁▁ 147.0 24.83
lab_CK_baseline_Q1 14 0.07 110.00 NA 110.0 110.00 110.00 110.00 110.0 ▁▁▇▁▁ 110.0 18.58
lab_CK_baseline_Q3 14 0.07 510.00 NA 510.0 510.00 510.00 510.00 510.0 ▁▁▇▁▁ 510.0 86.15
lab_CK_peak_n 14 0.07 15.00 NA 15.0 15.00 15.00 15.00 15.0 ▁▁▇▁▁ 15.0 2.53
lab_CK_peak_med 14 0.07 385.00 NA 385.0 385.00 385.00 385.00 385.0 ▁▁▇▁▁ 385.0 65.03
lab_CK_peak_Q1 14 0.07 117.00 NA 117.0 117.00 117.00 117.00 117.0 ▁▁▇▁▁ 117.0 19.76
lab_CK_peak_Q3 14 0.07 1615.00 NA 1615.0 1615.00 1615.00 1615.00 1615.0 ▁▁▇▁▁ 1615.0 272.80
lab_Ddim_baseline_n 13 0.13 18.50 2.12 17.0 17.75 18.50 19.25 20.0 ▇▁▁▁▇ 37.0 6.25
lab_Ddim_baseline_med 13 0.13 4642.00 907.93 4000.0 4321.00 4642.00 4963.00 5284.0 ▇▁▁▁▇ 9284.0 1568.24
lab_Ddim_baseline_Q1 14 0.07 4069.00 NA 4069.0 4069.00 4069.00 4069.00 4069.0 ▁▁▇▁▁ 4069.0 687.33
lab_Ddim_baseline_Q3 14 0.07 9095.00 NA 9095.0 9095.00 9095.00 9095.00 9095.0 ▁▁▇▁▁ 9095.0 1536.32
lab_Ddim_NS_n 7 0.53 34.12 11.41 20.0 27.50 33.00 36.25 58.0 ▃▇▂▁▂ 273.0 46.11
lab_Ddim_NS_med 7 0.53 3112.88 1124.58 1700.0 2325.00 3139.00 3781.25 4900.0 ▇▇▃▇▃ 24903.0 4206.59
lab_Ddim_NS_Q1 8 0.47 1791.86 881.26 800.0 1129.00 1700.00 2242.50 3300.0 ▇▂▂▂▂ 12543.0 2118.75
lab_Ddim_NS_Q3 8 0.47 5335.00 2878.01 2410.0 3450.00 4400.00 6667.50 10300.0 ▇▅▁▂▂ 37345.0 6308.28
lab_Ddim_peak_n 13 0.13 66.50 72.83 15.0 40.75 66.50 92.25 118.0 ▇▁▁▁▇ 133.0 22.47
lab_Ddim_peak_med 13 0.13 3075.00 1435.43 2060.0 2567.50 3075.00 3582.50 4090.0 ▇▁▁▁▇ 6150.0 1038.85
lab_Ddim_peak_Q1 13 0.13 1700.00 763.68 1160.0 1430.00 1700.00 1970.00 2240.0 ▇▁▁▁▇ 3400.0 574.32
lab_Ddim_peak_Q3 13 0.13 5507.25 4097.33 2610.0 4058.62 5507.25 6955.88 8404.5 ▇▁▁▁▇ 11014.5 1860.56
lab_fibrino_NS_n 10 0.33 26.40 8.17 17.0 18.00 31.00 33.00 33.0 ▅▁▁▁▇ 132.0 22.30
lab_fibrino_NS_med 10 0.33 655.20 92.14 551.0 596.00 627.00 736.00 766.0 ▃▇▁▁▇ 3276.0 553.38
lab_fibrino_NS_Q1 10 0.33 515.20 103.01 386.0 455.00 496.00 619.00 620.0 ▃▃▃▁▇ 2576.0 435.14
lab_fibrino_NS_Q3 10 0.33 779.20 76.22 689.0 719.00 782.00 836.00 870.0 ▇▁▃▁▇ 3896.0 658.11
lab_troponin_baseline_n 13 0.13 26.00 12.73 17.0 21.50 26.00 30.50 35.0 ▇▁▁▁▇ 52.0 8.78
lab_troponin_baseline_med 13 0.13 201.90 205.20 56.8 129.35 201.90 274.45 347.0 ▇▁▁▁▇ 403.8 68.21
lab_troponin_baseline_Q1 14 0.07 182.00 NA 182.0 182.00 182.00 182.00 182.0 ▁▁▇▁▁ 182.0 30.74
lab_troponin_baseline_Q3 14 0.07 1267.00 NA 1267.0 1267.00 1267.00 1267.00 1267.0 ▁▁▇▁▁ 1267.0 214.02
lab_troponin_peak_n 13 0.13 15.50 0.71 15.0 15.25 15.50 15.75 16.0 ▇▁▁▁▇ 31.0 5.24
lab_troponin_peak_med 13 0.13 402.00 8.49 396.0 399.00 402.00 405.00 408.0 ▇▁▁▁▇ 804.0 135.81
lab_troponin_peak_Q1 13 0.13 179.00 111.72 100.0 139.50 179.00 218.50 258.0 ▇▁▁▁▇ 358.0 60.47
lab_troponin_peak_Q3 13 0.13 979.50 424.97 679.0 829.25 979.50 1129.75 1280.0 ▇▁▁▁▇ 1959.0 330.91
lab_troponin_NS_n 8 0.47 28.00 17.56 7.0 15.00 33.00 34.00 58.0 ▅▂▇▁▂ 196.0 33.11
lab_troponin_NS_med 8 0.47 84.14 88.75 31.0 42.50 47.00 72.00 282.0 ▇▁▁▁▁ 589.0 99.49
lab_troponin_NS_Q1 9 0.40 24.33 17.96 6.0 11.00 20.50 34.50 52.0 ▇▇▁▃▃ 146.0 24.66
lab_troponin_NS_Q3 9 0.40 301.67 363.25 60.0 101.00 177.50 266.75 1023.0 ▇▂▁▁▂ 1810.0 305.74
lab_NTproBNP_baseline_n 12 0.20 12.67 6.66 5.0 10.50 16.00 16.50 17.0 ▃▁▁▁▇ 38.0 6.42
lab_NTproBNP_baseline_med 12 0.20 20548.33 19021.51 4328.0 10080.50 15833.00 28658.50 41484.0 ▇▇▁▁▇ 61645.0 10413.01
lab_NTproBNP_baseline_Q1 13 0.13 18964.00 23825.26 2117.0 10540.50 18964.00 27387.50 35811.0 ▇▁▁▁▇ 37928.0 6406.76
lab_NTproBNP_baseline_Q3 13 0.13 32922.50 27651.41 13370.0 23146.25 32922.50 42698.75 52475.0 ▇▁▁▁▇ 65845.0 11122.47
lab_NTproBNP_peak_n 13 0.13 15.50 0.71 15.0 15.25 15.50 15.75 16.0 ▇▁▁▁▇ 31.0 5.24
lab_NTproBNP_peak_med 13 0.13 19735.00 6696.30 15000.0 17367.50 19735.00 22102.50 24470.0 ▇▁▁▁▇ 39470.0 6667.23
lab_NTproBNP_peak_Q1 13 0.13 13270.50 5574.12 9329.0 11299.75 13270.50 15241.25 17212.0 ▇▁▁▁▇ 26541.0 4483.28
lab_NTproBNP_peak_Q3 13 0.13 20827.50 8241.33 15000.0 17913.75 20827.50 23741.25 26655.0 ▇▁▁▁▇ 41655.0 7036.32
lab_NTproBNP_NS_n 13 0.13 45.50 17.68 33.0 39.25 45.50 51.75 58.0 ▇▁▁▁▇ 91.0 15.37
lab_NTproBNP_NS_med 13 0.13 2056.50 1793.93 788.0 1422.25 2056.50 2690.75 3325.0 ▇▁▁▁▇ 4113.0 694.76
lab_NTproBNP_NS_Q1 13 0.13 407.00 329.51 174.0 290.50 407.00 523.50 640.0 ▇▁▁▁▇ 814.0 137.50
lab_NTproBNP_NS_Q3 13 0.13 8662.00 2667.21 6776.0 7719.00 8662.00 9605.00 10548.0 ▇▁▁▁▇ 17324.0 2926.35
lab_BNP_baseline_n 13 0.13 22.00 8.49 16.0 19.00 22.00 25.00 28.0 ▇▁▁▁▇ 44.0 7.43
lab_BNP_baseline_med 13 0.13 3065.50 3786.56 388.0 1726.75 3065.50 4404.25 5743.0 ▇▁▁▁▇ 6131.0 1035.64
lab_BNP_baseline_Q1 13 0.13 1361.50 1819.39 75.0 718.25 1361.50 2004.75 2648.0 ▇▁▁▁▇ 2723.0 459.97
lab_BNP_baseline_Q3 13 0.13 6497.50 7653.02 1086.0 3791.75 6497.50 9203.25 11909.0 ▇▁▁▁▇ 12995.0 2195.10
lab_BNP_peak_n 12 0.20 51.67 66.15 11.0 13.50 16.00 72.00 128.0 ▇▁▁▁▃ 155.0 26.18
lab_BNP_peak_med 12 0.20 2070.23 1905.37 760.0 977.35 1194.70 2725.35 4256.0 ▇▁▁▁▃ 6210.7 1049.10
lab_BNP_peak_Q1 12 0.20 1039.60 1126.18 388.0 389.40 390.80 1365.40 2340.0 ▇▁▁▁▃ 3118.8 526.82
lab_BNP_peak_Q3 12 0.20 4256.67 2583.18 1434.0 3133.50 4833.00 5668.00 6503.0 ▇▁▁▇▇ 12770.0 2157.09
lab_BNP_NS_n 11 0.27 11.25 4.79 7.0 8.50 10.00 12.75 18.0 ▇▃▁▁▃ 45.0 7.60
lab_BNP_NS_med 11 0.27 4262.50 2131.84 2231.0 3073.25 3805.00 4994.25 7209.0 ▇▇▇▁▇ 17050.0 2880.07
lab_BNP_NS_min 11 0.27 2442.75 2411.82 16.0 1252.00 2002.00 3192.75 5751.0 ▇▇▇▁▇ 9771.0 1650.51
lab_BNP_NS_max 11 0.27 8286.50 5441.64 3287.0 5699.00 6921.00 9508.50 16017.0 ▃▇▁▁▃ 33146.0 5598.99
lab_CRP_baseline_n 12 0.20 33.33 11.59 21.0 28.00 35.00 39.50 44.0 ▇▁▁▇▇ 100.0 16.89
lab_CRP_baseline_med 12 0.20 195.83 47.39 146.5 173.25 200.00 220.50 241.0 ▇▁▇▁▇ 587.5 99.24
lab_CRP_baseline_Q1 14 0.07 150.00 NA 150.0 150.00 150.00 150.00 150.0 ▁▁▇▁▁ 150.0 25.34
lab_CRP_baseline_Q3 14 0.07 311.00 NA 311.0 311.00 311.00 311.00 311.0 ▁▁▇▁▁ 311.0 52.53
lab_CRP_peak_n 11 0.27 66.00 71.67 15.0 28.50 38.50 76.00 172.0 ▇▁▁▁▂ 264.0 44.59
lab_CRP_peak_med 11 0.27 189.53 44.80 154.0 166.82 174.55 197.25 255.0 ▇▃▁▁▃ 758.1 128.06
lab_CRP_peak_Q1 12 0.20 146.00 30.32 128.0 128.50 129.00 155.00 181.0 ▇▁▁▁▃ 438.0 73.99
lab_CRP_peak_Q3 12 0.20 266.67 40.05 231.0 245.00 259.00 284.50 310.0 ▇▇▁▁▇ 800.0 135.14
lab_CRP_NS_n 5 0.67 29.40 14.95 7.0 22.25 32.00 34.50 58.0 ▃▃▇▂▂ 294.0 49.66
lab_CRP_NS_med 5 0.67 234.20 29.96 193.0 207.00 237.00 252.25 283.0 ▇▂▂▃▃ 2342.0 395.61
lab_CRP_NS_Q1 6 0.60 158.22 25.66 113.0 156.00 160.00 180.00 185.0 ▅▁▅▅▇ 1424.0 240.54
lab_CRP_NS_Q3 6 0.60 299.89 39.59 219.0 290.00 299.00 309.00 364.0 ▂▁▇▂▃ 2699.0 455.91
lab_ESR_baseline_n 14 0.07 44.00 NA 44.0 44.00 44.00 44.00 44.0 ▁▁▇▁▁ 44.0 7.43
lab_ESR_baseline_med 14 0.07 59.00 NA 59.0 59.00 59.00 59.00 59.0 ▁▁▇▁▁ 59.0 9.97
lab_ESR_peak_n 12 0.20 58.67 52.56 15.0 29.50 44.00 80.50 117.0 ▇▇▁▁▇ 176.0 29.73
lab_ESR_peak_med 12 0.20 69.67 5.03 65.0 67.00 69.00 72.00 75.0 ▇▇▁▁▇ 209.0 35.30
lab_ESR_peak_Q1 13 0.13 43.50 2.12 42.0 42.75 43.50 44.25 45.0 ▇▁▁▁▇ 87.0 14.70
lab_ESR_peak_Q3 13 0.13 90.50 0.71 90.0 90.25 90.50 90.75 91.0 ▇▁▁▁▇ 181.0 30.57
lab_ESR_NS_n 11 0.27 21.25 8.22 14.0 17.00 19.00 23.25 33.0 ▃▇▁▁▃ 85.0 14.36
lab_ESR_NS_med 11 0.27 57.75 5.17 53.0 54.88 56.50 59.38 65.0 ▃▇▁▁▃ 231.0 39.02
lab_ESR_NS_Q1 11 0.27 42.80 13.02 28.2 35.55 42.00 49.25 59.0 ▇▇▇▁▇ 171.2 28.92
lab_ESR_NS_Q3 11 0.27 78.17 6.90 72.0 74.62 76.35 79.90 88.0 ▃▇▁▁▃ 312.7 52.82
lab_PCT_baseline_n 13 0.13 23.50 3.54 21.0 22.25 23.50 24.75 26.0 ▇▁▁▁▇ 47.0 7.94
lab_PCT_baseline_med 13 0.13 28.85 10.11 21.7 25.27 28.85 32.42 36.0 ▇▁▁▁▇ 57.7 9.75
lab_PCT_baseline_Q1 14 0.07 8.00 NA 8.0 8.00 8.00 8.00 8.0 ▁▁▇▁▁ 8.0 1.35
lab_PCT_baseline_Q3 14 0.07 99.00 NA 99.0 99.00 99.00 99.00 99.0 ▁▁▇▁▁ 99.0 16.72
lab_PCT_peak_n 14 0.07 33.00 NA 33.0 33.00 33.00 33.00 33.0 ▁▁▇▁▁ 33.0 5.57
lab_PCT_peak_med 14 0.07 6.00 NA 6.0 6.00 6.00 6.00 6.0 ▁▁▇▁▁ 6.0 1.01
lab_PCT_peak_Q1 14 0.07 2.70 NA 2.7 2.70 2.70 2.70 2.7 ▁▁▇▁▁ 2.7 0.46
lab_PCT_peak_Q3 14 0.07 16.50 NA 16.5 16.50 16.50 16.50 16.5 ▁▁▇▁▁ 16.5 2.79
lab_PCT_NS_n 9 0.40 25.33 7.28 16.0 20.25 25.00 32.00 33.0 ▃▇▁▃▇ 152.0 25.68
lab_PCT_NS_med 9 0.40 10.01 7.10 2.7 5.58 8.70 11.86 22.5 ▇▁▅▁▂ 60.0 10.14
lab_PCT_NS_Q1 10 0.33 2.33 1.47 0.4 1.80 2.20 2.87 4.4 ▇▇▇▇▇ 11.7 1.97
lab_PCT_NS_Q3 10 0.33 20.15 4.42 15.8 16.70 18.50 24.80 25.0 ▇▃▁▁▇ 100.8 17.02
lab_IL6_baseline_n 11 0.27 19.50 7.90 13.0 16.00 17.00 20.50 31.0 ▃▇▁▁▃ 78.0 13.18
lab_IL6_baseline_med 11 0.27 187.62 43.10 135.0 161.25 194.60 220.97 226.3 ▃▃▁▁▇ 750.5 126.77
lab_IL6_baseline_Q1 14 0.07 87.00 NA 87.0 87.00 87.00 87.00 87.0 ▁▁▇▁▁ 87.0 14.70
lab_IL6_baseline_Q3 14 0.07 175.00 NA 175.0 175.00 175.00 175.00 175.0 ▁▁▇▁▁ 175.0 29.56
lab_IL6_NS_n 11 0.27 16.75 11.84 7.0 8.50 13.50 21.75 33.0 ▇▁▃▁▃ 67.0 11.32
lab_IL6_NS_med 11 0.27 151.50 66.17 80.0 103.25 155.50 203.75 215.0 ▃▃▁▁▇ 606.0 102.36
lab_IL6_NS_Q1 11 0.27 43.70 24.95 7.7 37.17 51.70 58.22 63.7 ▃▁▁▃▇ 174.8 29.53
lab_IL6_NS_Q3 11 0.27 308.10 18.80 286.0 297.55 308.20 318.75 330.0 ▇▇▁▇▇ 1232.4 208.18
lab_IL1_NS_n 14 0.07 33.00 NA 33.0 33.00 33.00 33.00 33.0 ▁▁▇▁▁ 33.0 5.57
lab_IL1_NS_med 14 0.07 0.80 NA 0.8 0.80 0.80 0.80 0.8 ▁▁▇▁▁ 0.8 0.14
lab_IL1_NS_Q1 14 0.07 0.40 NA 0.4 0.40 0.40 0.40 0.4 ▁▁▇▁▁ 0.4 0.07
lab_IL1_NS_Q3 14 0.07 1.20 NA 1.2 1.20 1.20 1.20 1.2 ▁▁▇▁▁ 1.2 0.20
lab_IL8_NS_n 14 0.07 33.00 NA 33.0 33.00 33.00 33.00 33.0 ▁▁▇▁▁ 33.0 5.57
lab_IL8_NS_med 14 0.07 41.70 NA 41.7 41.70 41.70 41.70 41.7 ▁▁▇▁▁ 41.7 7.04
lab_IL8_NS_Q1 14 0.07 25.10 NA 25.1 25.10 25.10 25.10 25.1 ▁▁▇▁▁ 25.1 4.24
lab_IL8_NS_Q3 14 0.07 54.40 NA 54.4 54.40 54.40 54.40 54.4 ▁▁▇▁▁ 54.4 9.19
admis_days_hosp_med 9 0.40 7.98 2.75 4.0 7.03 7.45 9.45 12.0 ▃▇▃▃▃ 47.9 8.09
admis_days_hosp_Q1 9 0.40 5.67 2.42 3.0 4.00 5.00 7.50 9.0 ▇▁▂▁▅ 34.0 5.74
admis_days_hosp_Q3 9 0.40 12.18 3.59 8.0 10.03 11.55 13.75 18.0 ▇▃▇▁▃ 73.1 12.35
admis_days_ICU_n 3 0.80 32.83 37.52 7.0 16.50 23.00 33.50 148.0 ▇▁▁▁▁ 394.0 66.55
admis_days_ICU_med 7 0.53 5.70 1.03 4.0 4.92 6.00 6.43 7.0 ▂▅▁▇▅ 45.6 7.70
admis_days_ICU_min 14 0.07 3.00 NA 3.0 3.00 3.00 3.00 3.0 ▁▁▇▁▁ 3.0 0.51
admis_days_ICU_Q1 9 0.40 3.95 1.10 3.0 3.17 3.85 4.00 6.0 ▅▇▁▁▂ 23.7 4.00
admis_days_ICU_Q3 9 0.40 8.50 1.97 5.0 8.00 9.00 10.00 10.0 ▂▁▅▁▇ 51.0 8.61
admis_days_ICU_max 14 0.07 12.00 NA 12.0 12.00 12.00 12.00 12.0 ▁▁▇▁▁ 12.0 2.03
critcare_NIV_n 7 0.53 6.62 5.01 0.0 2.75 6.00 11.25 13.0 ▅▅▁▂▇ 53.0 8.95
critcare_MV_n 0 1.00 8.67 10.81 0.0 2.50 4.00 9.00 37.0 ▇▁▁▁▁ 130.0 21.96
critcare_MV_med 13 0.13 3.00 0.00 3.0 3.00 3.00 3.00 3.0 ▁▁▇▁▁ 6.0 1.01
critcare_MV_min 15 0.00 NaN NaN NA NA NA NA NA 0.0 0.00
critcare_MV_max 15 0.00 NaN NaN NA NA NA NA NA 0.0 0.00
critcare_inotrop_n 1 0.93 21.57 21.60 0.0 10.00 17.00 26.50 90.0 ▇▅▁▁▁ 302.0 51.01
critcare_inotrop_med 12 0.20 3.00 0.00 3.0 3.00 3.00 3.00 3.0 ▁▁▇▁▁ 9.0 1.52
critcare_inotrop_Q1 12 0.20 2.33 0.58 2.0 2.00 2.00 2.50 3.0 ▇▁▁▁▃ 7.0 1.18
critcare_inotrop_Q3 12 0.20 4.50 1.50 3.0 3.75 4.50 5.25 6.0 ▇▁▇▁▇ 13.5 2.28
critcare_ECMO_n 5 0.67 2.80 3.43 0.0 1.00 1.50 2.75 10.0 ▇▁▁▁▁ 28.0 4.73
critcare_ECMO_med 14 0.07 4.50 NA 4.5 4.50 4.50 4.50 4.5 ▁▁▇▁▁ 4.5 0.76
critcare_ECMO_Q1 14 0.07 3.00 NA 3.0 3.00 3.00 3.00 3.0 ▁▁▇▁▁ 3.0 0.51
critcare_ECMO_Q3 14 0.07 6.00 NA 6.0 6.00 6.00 6.00 6.0 ▁▁▇▁▁ 6.0 1.01
critcare_RRT_n 12 0.20 2.33 3.21 0.0 0.50 1.00 3.50 6.0 ▇▁▁▁▃ 7.0 1.18
rx_cortic_n 1 0.93 22.71 23.09 1.0 10.50 16.50 28.25 91.0 ▇▂▁▁▁ 318.0 53.72
rx_aspirin_low_n 10 0.33 9.00 7.31 4.0 4.00 5.00 11.00 21.0 ▇▁▂▁▂ 45.0 7.60
rx_aspirin_high_n 12 0.20 2.67 0.58 2.0 2.50 3.00 3.00 3.0 ▃▁▁▁▇ 8.0 1.35
rx_aspirin_NS 14 0.07 29.00 NA 29.0 29.00 29.00 29.00 29.0 ▁▁▇▁▁ 29.0 4.90
rx_heparin_n 9 0.40 28.00 30.98 0.0 11.75 18.50 30.50 87.0 ▇▅▁▁▂ 168.0 28.38
rx_IVIg_once_n 1 0.93 30.36 34.44 7.0 13.00 23.00 32.25 144.0 ▇▂▁▁▁ 425.0 71.79
rx_IVIg_multip_n 8 0.47 9.00 13.67 1.0 1.50 4.00 8.00 39.0 ▇▂▁▁▂ 63.0 10.64
rx_anakinra_n 8 0.47 6.71 7.91 1.0 3.00 4.00 6.00 24.0 ▇▂▁▁▂ 47.0 7.94
rx_infliximab_n 12 0.20 3.00 4.36 0.0 0.50 1.00 4.50 8.0 ▇▁▁▁▃ 9.0 1.52
rx_tocilizumab_n 10 0.33 6.20 6.30 1.0 1.00 3.00 12.00 14.0 ▇▁▁▁▅ 31.0 5.24
rx_antibiotics_n 12 0.20 20.67 7.37 15.0 16.50 18.00 23.50 29.0 ▇▇▁▁▇ 62.0 10.47
rx_plasma_n 14 0.07 1.00 NA 1.0 1.00 1.00 1.00 1.0 ▁▁▇▁▁ 1.0 0.17
rx_remdesivir_n 14 0.07 7.00 NA 7.0 7.00 7.00 7.00 7.0 ▁▁▇▁▁ 7.0 1.18
outcome_death_n 0 1.00 0.60 1.06 0.0 0.00 0.00 1.00 4.0 ▇▅▁▁▁ 9.0 1.52
outcome_LVdysfunc_n 8 0.47 2.86 3.13 0.0 1.00 2.00 3.50 9.0 ▇▅▂▁▂ 20.0 3.38

3.4 Historical controls

Data summary
Name df_cohort_controls_stats
Number of rows 2
Number of columns 80
_______________________
Column type frequency:
character 7
numeric 73
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
doi 0 1 35 39 0 2 0
first_author 0 1 18 18 0 2 0
journal 0 1 4 13 0 2 0
cohort_id 0 1 18 19 0 2 0
cohort_type 0 1 7 7 0 1 0
prim_input 0 1 2 2 0 1 0
cross_check 0 1 3 3 0 1 0

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist sum Prct_total
date_of_publication 0 1.0 43991.50 2.12 43990.0 43990.75 43991.50 43992.25 43993.0 ▇▁▁▁▇ 87983.0 6507.62
tot_cases_n 0 1.0 676.00 644.88 220.0 448.00 676.00 904.00 1132.0 ▇▁▁▁▇ 1352.0 100.00
sex_m 1 0.5 128.00 NA 128.0 128.00 128.00 128.00 128.0 ▁▁▇▁▁ 128.0 9.47
sex_f 1 0.5 92.00 NA 92.0 92.00 92.00 92.00 92.0 ▁▁▇▁▁ 92.0 6.80
age_med_yrs 0 1.0 2.35 0.49 2.0 2.17 2.35 2.53 2.7 ▇▁▁▁▇ 4.7 0.35
age_Q1_yrs 0 1.0 1.30 0.14 1.2 1.25 1.30 1.35 1.4 ▇▁▁▁▇ 2.6 0.19
age_Q3_yrs 0 1.0 4.15 0.78 3.6 3.88 4.15 4.43 4.7 ▇▁▁▁▇ 8.3 0.61
race_n_white 1 0.5 48.00 NA 48.0 48.00 48.00 48.00 48.0 ▁▁▇▁▁ 48.0 3.55
race_n_black 1 0.5 67.00 NA 67.0 67.00 67.00 67.00 67.0 ▁▁▇▁▁ 67.0 4.96
race_n_asian 1 0.5 28.00 NA 28.0 28.00 28.00 28.00 28.0 ▁▁▇▁▁ 28.0 2.07
race_n_other 1 0.5 44.00 NA 44.0 44.00 44.00 44.00 44.0 ▁▁▇▁▁ 44.0 3.25
symp_cardiovasc_myocard_n 1 0.5 3.00 NA 3.0 3.00 3.00 3.00 3.0 ▁▁▇▁▁ 3.0 0.22
symp_cardiovasc_pericard_n 1 0.5 15.00 NA 15.0 15.00 15.00 15.00 15.0 ▁▁▇▁▁ 15.0 1.11
symp_cardiovasc_cordilat_n 1 0.5 42.00 NA 42.0 42.00 42.00 42.00 42.0 ▁▁▇▁▁ 42.0 3.11
kawasaki_complete_n 1 0.5 142.00 NA 142.0 142.00 142.00 142.00 142.0 ▁▁▇▁▁ 142.0 10.50
kawasaki_incomplete_n 1 0.5 78.00 NA 78.0 78.00 78.00 78.00 78.0 ▁▁▇▁▁ 78.0 5.77
kawasaki_exanthema_n 1 0.5 187.00 NA 187.0 187.00 187.00 187.00 187.0 ▁▁▇▁▁ 187.0 13.83
kawasaki_mouth_n 1 0.5 189.00 NA 189.0 189.00 189.00 189.00 189.0 ▁▁▇▁▁ 189.0 13.98
kawasaki_extremity_n 1 0.5 120.00 NA 120.0 120.00 120.00 120.00 120.0 ▁▁▇▁▁ 120.0 8.88
kawasaki_cervical_n 1 0.5 114.00 NA 114.0 114.00 114.00 114.00 114.0 ▁▁▇▁▁ 114.0 8.43
kawasaki_conjunctivitis_n 1 0.5 176.00 NA 176.0 176.00 176.00 176.00 176.0 ▁▁▇▁▁ 176.0 13.02
lab_Hb_NS_n 1 0.5 1132.00 NA 1132.0 1132.00 1132.00 1132.00 1132.0 ▁▁▇▁▁ 1132.0 83.73
lab_Hb_NS_med 1 0.5 11.10 NA 11.1 11.10 11.10 11.10 11.1 ▁▁▇▁▁ 11.1 0.82
lab_Hb_NS_Q1 1 0.5 10.50 NA 10.5 10.50 10.50 10.50 10.5 ▁▁▇▁▁ 10.5 0.78
lab_Hb_NS_Q3 1 0.5 11.90 NA 11.9 11.90 11.90 11.90 11.9 ▁▁▇▁▁ 11.9 0.88
lab_WBC_NS_n 1 0.5 1132.00 NA 1132.0 1132.00 1132.00 1132.00 1132.0 ▁▁▇▁▁ 1132.0 83.73
lab_WBC_NS_med 1 0.5 13400.00 NA 13400.0 13400.00 13400.00 13400.00 13400.0 ▁▁▇▁▁ 13400.0 991.12
lab_WBC_NS_Q1 1 0.5 10500.00 NA 10500.0 10500.00 10500.00 10500.00 10500.0 ▁▁▇▁▁ 10500.0 776.63
lab_WBC_NS_Q3 1 0.5 17300.00 NA 17300.0 17300.00 17300.00 17300.00 17300.0 ▁▁▇▁▁ 17300.0 1279.59
lab_neutro_NS_n 1 0.5 1132.00 NA 1132.0 1132.00 1132.00 1132.00 1132.0 ▁▁▇▁▁ 1132.0 83.73
lab_neutro_NS_med 1 0.5 7200.00 NA 7200.0 7200.00 7200.00 7200.00 7200.0 ▁▁▇▁▁ 7200.0 532.54
lab_neutro_NS_Q1 1 0.5 5100.00 NA 5100.0 5100.00 5100.00 5100.00 5100.0 ▁▁▇▁▁ 5100.0 377.22
lab_neutro_NS_Q3 1 0.5 9900.00 NA 9900.0 9900.00 9900.00 9900.00 9900.0 ▁▁▇▁▁ 9900.0 732.25
lab_lympho_NS_n 0 1.0 676.00 644.88 220.0 448.00 676.00 904.00 1132.0 ▇▁▁▁▇ 1352.0 100.00
lab_lympho_NS_med 0 1.0 2940.00 197.99 2800.0 2870.00 2940.00 3010.00 3080.0 ▇▁▁▁▇ 5880.0 434.91
lab_lympho_NS_Q1 0 1.0 1680.00 254.56 1500.0 1590.00 1680.00 1770.00 1860.0 ▇▁▁▁▇ 3360.0 248.52
lab_lympho_NS_Q3 0 1.0 4585.00 261.63 4400.0 4492.50 4585.00 4677.50 4770.0 ▇▁▁▁▇ 9170.0 678.25
lab_platelet_NS_n 0 1.0 676.00 644.88 220.0 448.00 676.00 904.00 1132.0 ▇▁▁▁▇ 1352.0 100.00
lab_platelet_NS_med 0 1.0 374000.00 12727.92 365000.0 369500.00 374000.00 378500.00 383000.0 ▇▁▁▁▇ 748000.0 55325.44
lab_platelet_NS_Q1 0 1.0 288500.00 707.11 288000.0 288250.00 288500.00 288750.00 289000.0 ▇▁▁▁▇ 577000.0 42677.51
lab_platelet_NS_Q3 0 1.0 476500.00 20506.10 462000.0 469250.00 476500.00 483750.00 491000.0 ▇▁▁▁▇ 953000.0 70488.17
lab_sodium_NS_n 1 0.5 220.00 NA 220.0 220.00 220.00 220.00 220.0 ▁▁▇▁▁ 220.0 16.27
lab_sodium_NS_med 1 0.5 135.00 NA 135.0 135.00 135.00 135.00 135.0 ▁▁▇▁▁ 135.0 9.99
lab_sodium_NS_Q1 1 0.5 134.00 NA 134.0 134.00 134.00 134.00 134.0 ▁▁▇▁▁ 134.0 9.91
lab_sodium_NS_Q3 1 0.5 137.00 NA 137.0 137.00 137.00 137.00 137.0 ▁▁▇▁▁ 137.0 10.13
lab_ferritin_NS_n 1 0.5 1132.00 NA 1132.0 1132.00 1132.00 1132.00 1132.0 ▁▁▇▁▁ 1132.0 83.73
lab_ferritin_NS_med 1 0.5 200.00 NA 200.0 200.00 200.00 200.00 200.0 ▁▁▇▁▁ 200.0 14.79
lab_ferritin_NS_Q1 1 0.5 143.00 NA 143.0 143.00 143.00 143.00 143.0 ▁▁▇▁▁ 143.0 10.58
lab_ferritin_NS_Q3 1 0.5 243.00 NA 243.0 243.00 243.00 243.00 243.0 ▁▁▇▁▁ 243.0 17.97
lab_ALT_NS_n 1 0.5 1132.00 NA 1132.0 1132.00 1132.00 1132.00 1132.0 ▁▁▇▁▁ 1132.0 83.73
lab_ALT_NS_median 1 0.5 42.00 NA 42.0 42.00 42.00 42.00 42.0 ▁▁▇▁▁ 42.0 3.11
lab_ALT_NS_Q1 1 0.5 24.00 NA 24.0 24.00 24.00 24.00 24.0 ▁▁▇▁▁ 24.0 1.78
lab_ALT_NS_Q3 1 0.5 112.00 NA 112.0 112.00 112.00 112.00 112.0 ▁▁▇▁▁ 112.0 8.28
lab_albumin_NS_n 0 1.0 676.00 644.88 220.0 448.00 676.00 904.00 1132.0 ▇▁▁▁▇ 1352.0 100.00
lab_albumin_NS_med 0 1.0 31.50 9.19 25.0 28.25 31.50 34.75 38.0 ▇▁▁▁▇ 63.0 4.66
lab_albumin_NS_Q1 0 1.0 28.50 9.19 22.0 25.25 28.50 31.75 35.0 ▇▁▁▁▇ 57.0 4.22
lab_albumin_NS_Q3 0 1.0 34.50 9.19 28.0 31.25 34.50 37.75 41.0 ▇▁▁▁▇ 69.0 5.10
lab_Ddim_NS_n 1 0.5 1132.00 NA 1132.0 1132.00 1132.00 1132.00 1132.0 ▁▁▇▁▁ 1132.0 83.73
lab_Ddim_NS_med 1 0.5 1650.00 NA 1650.0 1650.00 1650.00 1650.00 1650.0 ▁▁▇▁▁ 1650.0 122.04
lab_Ddim_NS_Q1 1 0.5 970.00 NA 970.0 970.00 970.00 970.00 970.0 ▁▁▇▁▁ 970.0 71.75
lab_Ddim_NS_Q3 1 0.5 2660.00 NA 2660.0 2660.00 2660.00 2660.00 2660.0 ▁▁▇▁▁ 2660.0 196.75
lab_troponin_NS_n 1 0.5 1132.00 NA 1132.0 1132.00 1132.00 1132.00 1132.0 ▁▁▇▁▁ 1132.0 83.73
lab_troponin_NS_med 1 0.5 10.00 NA 10.0 10.00 10.00 10.00 10.0 ▁▁▇▁▁ 10.0 0.74
lab_troponin_NS_Q1 1 0.5 10.00 NA 10.0 10.00 10.00 10.00 10.0 ▁▁▇▁▁ 10.0 0.74
lab_troponin_NS_Q3 1 0.5 20.00 NA 20.0 20.00 20.00 20.00 20.0 ▁▁▇▁▁ 20.0 1.48
lab_NTproBNP_NS_n 1 0.5 1132.00 NA 1132.0 1132.00 1132.00 1132.00 1132.0 ▁▁▇▁▁ 1132.0 83.73
lab_NTproBNP_NS_med 1 0.5 41.00 NA 41.0 41.00 41.00 41.00 41.0 ▁▁▇▁▁ 41.0 3.03
lab_NTproBNP_NS_Q1 1 0.5 12.00 NA 12.0 12.00 12.00 12.00 12.0 ▁▁▇▁▁ 12.0 0.89
lab_NTproBNP_NS_Q3 1 0.5 102.00 NA 102.0 102.00 102.00 102.00 102.0 ▁▁▇▁▁ 102.0 7.54
lab_CRP_NS_n 0 1.0 676.00 644.88 220.0 448.00 676.00 904.00 1132.0 ▇▁▁▁▇ 1352.0 100.00
lab_CRP_NS_med 0 1.0 104.50 53.03 67.0 85.75 104.50 123.25 142.0 ▇▁▁▁▇ 209.0 15.46
lab_CRP_NS_Q1 0 1.0 67.50 38.89 40.0 53.75 67.50 81.25 95.0 ▇▁▁▁▇ 135.0 9.99
lab_CRP_NS_Q3 0 1.0 173.50 33.23 150.0 161.75 173.50 185.25 197.0 ▇▁▁▁▇ 347.0 25.67

4 Data exploration

4.2 Sex

n_cohort <- df_cohort %>% select(tot_cases_n) %>% sum()
var_cohort <- df_cohort %>% select(contains("sex"))
var_cohort <- colSums(var_cohort, na.rm = TRUE)
var_cohort <- var_cohort/sum(df_cohort$tot_cases_n)*100
var_cohort["sex_na"] <- (100 - var_cohort["sex_m"] - var_cohort["sex_f"])

var_control <- df_cohort_controls %>% filter(cohort_id == "Pouletty - control") %>% select(contains("sex"))
var_control <- colSums(var_control, na.rm = TRUE)
var_control <- var_control/sum(df_cohort_controls %>% filter(cohort_id == "Pouletty - control") %>% select(tot_cases_n))*100
var_control["sex_na"] <- (100 - var_control["sex_m"] - var_control["sex_f"])

n_single <- df_singlecases %>% nrow()
var_single <- df_singlecases %>% select(contains("sex"))
var_single$sex_m <- ifelse(var_single$sex == "M", TRUE, FALSE)
var_single$sex_f <- ifelse(var_single$sex == "F", TRUE, FALSE)
cols <- sapply(var_single, is.logical)
var_single[,cols] <- lapply(var_single[,cols], as.numeric)
var_single <- colSums(var_single %>% select(-sex), na.rm = TRUE)
var_single <- var_single/nrow(df_singlecases)*100
var_single["sex_na"] <- (100 - var_single["sex_m"] - var_single["sex_f"])

bar_df_prct <- data.frame(
  x = c("males", "females", "missing", "males", "females", "missing", "males", "females", "missing"),
  vals = c(var_single, var_cohort, var_control),
  col = c(rep("single", length(var_single)), rep("cohorts", length(var_cohort)), rep("histor ctrl", length(var_control))
))

p_prct <- ggplot(bar_df_prct, aes(x = col, y =  vals, fill = x)) +
    geom_bar(stat = "identity", position = "stack") +
    theme_bw() + 
    labs(title = "Male/female distribution in dataset", subtitle = "Prct", x = "sex", y = "%", col = " ")  + lims(y = c(0,100)) + theme(axis.text.x=element_text(angle=90, hjust=1))+
  scale_fill_manual(values = wes_palette("Royal1"))
p_prct

var_cohort <- df_cohort %>% select(contains("sex") | ("cohort_id") | "tot_cases_n")
sex_f <- var_cohort %>% group_by(cohort_id) %>% summarize(prct = sex_f/tot_cases_n) %>%  mutate(sex = "female")
sex_m <- var_cohort %>% group_by(cohort_id) %>% summarize(prct = sex_m/tot_cases_n) %>% mutate(sex = "male")
sex_all <- rbind(sex_f, sex_m)

p_sex_cohort <- ggplot(sex_all, aes(y = cohort_id, x = prct, fill = sex)) + 
          geom_bar(stat = "identity", position = "fill") + 
          theme_bw() + labs(x = "") + 
          scale_fill_manual(values = wes_palette("Royal1"))

var_controls <- df_cohort_controls %>% filter(cohort_id == "Pouletty - control") %>% select(contains("sex") | ("cohort_id") | "tot_cases_n")
sex_f <- var_controls %>% group_by(cohort_id) %>% summarize(prct = sex_f/tot_cases_n) %>% mutate(sex = "female")
sex_m <- var_controls %>% group_by(cohort_id) %>% summarize(prct = sex_m/tot_cases_n) %>% mutate(sex = "male")
sex_all <- rbind(sex_f, sex_m)

p_sex_controls <- ggplot(sex_all, aes(y = cohort_id, x = prct, fill = sex)) + 
          geom_bar(stat = "identity", position = "fill") + 
          theme_bw() + labs(x = "") + 
          scale_fill_manual(values = wes_palette("Royal1"))

n_single <- df_singlecases %>% nrow()
var_single <- df_singlecases %>% select(contains("sex"))
var_single$sex_m <- ifelse(var_single$sex == "M", TRUE, FALSE)
var_single$sex_f <- ifelse(var_single$sex == "F", TRUE, FALSE)
cols <- sapply(var_single, is.logical)
var_single[,cols] <- lapply(var_single[,cols], as.numeric)
var_single <- colSums(var_single %>% select(-sex), na.rm = TRUE)
var_single <- var_single/nrow(df_singlecases)*100

sex_single <- data.frame(cohort_id = "single_cases", prct = c(var_single["sex_m"], var_single["sex_f"]), sex = c("male", "female"))

p_sex_single <- ggplot(sex_single, aes(y = cohort_id, x = prct, fill = sex)) + 
          geom_bar(stat = "identity", position = "fill") + 
          theme_bw() + 
          scale_fill_manual(values = wes_palette("Royal1"))

a <- plot_grid(p_sex_cohort, p_sex_controls, p_sex_single, align = "v", nrow = 3, rel_heights = c(5/7, 1/7, 1/7))
a

4.3 Age distribution

cohort_age <- df_cohort_controls %>% select(contains("cohort_id") | contains("age") | contains("cohort_type")  | contains("tot_cases_n"))
cohort_age$cohort_id <- paste0(cohort_age$cohort_id, " (n = ", cohort_age$tot_cases_n,")")
cohort_age$age_med_yrs <- as.numeric(cohort_age$age_med_yrs )
cohort_age$age_Q1_yrs <- as.numeric(cohort_age$age_Q1_yrs)
cohort_age$age_Q3_yrs <- as.numeric(cohort_age$age_Q3_yrs)
cohort_age$age_min_yrs <- as.numeric(cohort_age$age_min_yrs)
cohort_age$age_max_yrs <- as.numeric(cohort_age$age_max_yrs)

cohort_age$data_descr <- ifelse(!is.na(cohort_age$age_Q1_yrs) & is.na(cohort_age$age_min_yrs) , "IQR", 
                                ifelse(is.na(cohort_age$age_Q1_yrs) & !is.na(cohort_age$age_min_yrs), "range", 
                                    ifelse(!is.na(cohort_age$age_Q1_yrs) & !is.na(cohort_age$age_min_yrs), "both", "none")))

p_age_cohort <- ggplot(cohort_age %>% filter(cohort_type == "covid"), aes(y = cohort_id, x = age_med_yrs, col = data_descr)) + 
        geom_point(size = 4) + 
        geom_errorbar(aes(xmin=age_Q1_yrs, xmax=age_Q3_yrs), width=.8, position=position_dodge(.9)) +
        geom_errorbar(aes(xmin=age_min_yrs,  xmax=age_max_yrs), width=.2, position=position_dodge(.9)) +
        theme_bw() + lims(x = c(0,21)) + 
        labs(y = "cohort", x = "", col = "bars") + theme(legend.position="top")+
        scale_color_manual(values = c(wes_palette("BottleRocket2")[1:3], wes_palette("BottleRocket1")[2]))

p_age_controls <- ggplot(cohort_age %>% filter(cohort_type != "covid"), aes(y = cohort_id, x = age_med_yrs, col = data_descr)) + 
        geom_point(size = 4) + 
        geom_errorbar(aes(xmin=age_Q1_yrs, xmax=age_Q3_yrs), width=.2, position=position_dodge(.9)) +
        geom_errorbar(aes(xmin=age_min_yrs,  xmax=age_max_yrs), width=.2, position=position_dodge(.9)) +
        theme_bw() + lims(x = c(0,21)) +
        labs(y = "cohort", x = "", col = "bars") + theme(legend.position="none")+
        scale_color_manual(values = wes_palette("BottleRocket2")[2])

p_age_single <- ggplot(df_singlecases, aes(x = as.numeric(age), y = paste0("single cases (n = ", n_single,")"))) +
      geom_violin(fill = wes_palette("Darjeeling2")[4]) + 
      geom_boxplot(width=.3, fill = wes_palette("Darjeeling2")[1]) + 
      theme_bw() + geom_beeswarm(groupOnX=FALSE, alpha = 0.5) + lims(x = c(0,21)) + 
      labs(y = "cohort", x = "Age (years)")

a <- plot_grid(p_age_cohort, p_age_controls, p_age_single, align = "v", nrow = 3, rel_heights = c(2/3, 1/5, 1/3))
a

4.4 Symptoms

4.4.2 Single cases + cohort

4.4.2.1 Respiratory

barSymp <- function(colname_chort, colname_single, exclude_single = NULL, plottitle){

var_cohort <- df_cohort %>% 
                        select(contains("cohort_id") | contains("tot_cases_n") | (contains(colname_chort) & contains("_n")))

var_cohort <- var_cohort %>% 
        gather(variable, value, 3:ncol(var_cohort)) %>% 
        drop_na(value)  %>% group_by(variable) %>% 
        summarize(prct = sum(value)/sum(tot_cases_n)*100)

var_cohort <- setNames(var_cohort$prct, var_cohort$variable)
names(var_cohort) <- sub("_n", "", names(var_cohort))

n_single <- df_singlecases %>% nrow()

if (!is.null(exclude_single)){
  var_single <- df_singlecases %>% select(-contains(exclude_single))
  var_single <- var_single %>% select(contains(colname_single))
} else
{
  var_single <- df_singlecases %>% select(contains(colname_single))
}

 #%>% select(-contains("any"))
cols <- sapply(var_single, is.logical)
var_single[,cols] <- lapply(var_single[,cols], as.numeric)
var_single <- colSums(var_single, na.rm = TRUE)
var_single <- var_single/nrow(df_singlecases)*100

bar_df_prct <- data.frame(
  x = c(names(var_single), names(var_cohort)),
  vals = c(var_single, var_cohort),
  col = c(rep("single", length(var_single)), rep("cohorts", length(var_cohort)))
)

p_prct <- ggplot(bar_df_prct, aes(x = x, y =  vals, fill = col)) +
    geom_bar(stat = "identity", position = "dodge") +
    theme_bw() + 
    labs(title = plottitle, 
          subtitle = "Percent of group", x = "treatment", y = "%", col = " ")  + 
          theme(axis.text.x=element_text(angle=90, hjust=1))+
          scale_fill_manual(values = wes_palette("Royal1"))
p_prct
}

makeHeatmap_cohort("symp_resp", "symp_resp", plottitle = "Cases with respiratory symptoms, per cohort")

4.5 COVID contact

var_cohort <- df_cohort %>% select(("cohort_id" | "tot_cases_n") | ( contains("covid") & contains("_n") & (contains("pos") | contains("closecont")  | contains("any"))))
var_cohort$cohort_id <- paste0(var_cohort$cohort_id, " (n = ", as.character(var_cohort$tot_cases_n),")")

var_cohort <- var_cohort %>% 
  gather(variable, value, 3:ncol(var_cohort)) %>% group_by(cohort_id, variable) %>% summarize(prct = value/tot_cases_n*100)

var_cohort$variable <- sub("n_", "", var_cohort$variable)

var_single <- df_singlecases %>% select(contains("covid"))
cols <- sapply(var_single, is.logical)
var_single[,cols] <- lapply(var_single[,cols], as.numeric)
var_single <- colSums(var_single, na.rm = TRUE)
var_single <- var_single/nrow(df_singlecases)*100
var_single <- as.data.frame(var_single) %>% rownames_to_column()
var_single$cohort_id <- "single_cases"
colnames(var_single) <- c("variable", "prct", "cohort_id")


missing <- setdiff(var_single$variable, var_cohort$variable)
if (length(missing) != 0 ){
  missing_df <- data.frame(variable = missing, prct = rep(NA, length(missing)), cohort_id = rep(unique(var_cohort$cohort_id), length(missing)))
  var_cohort <- bind_rows(var_cohort, as_tibble(missing_df))
} 

missing <- setdiff(var_cohort$variable, var_single$variable)

if (length(missing) != 0) {
  if (length(missing) != 0){
data.frame(variable = missing, prct = rep(NA, length(missing)), cohort_id = rep(unique(var_single$cohort_id), length(missing)))
  var_single <- bind_rows(var_single, as_tibble(missing_df))
  }
}


hm_cohort <- ggplot(var_cohort, aes(x = variable, y = cohort_id, fill = prct)) + 
    geom_tile() + theme_classic() +
    theme(axis.text.x=element_blank(), axis.ticks.x=element_blank(), axis.line=element_blank())+
   scale_fill_gradient(low = "yellow", high="red", na.value = "lightgray", limits = c(0,100)) +
    labs(x = "", y = "cohort", title = "COVID symptoms, per cohort") +
    geom_text(aes(label=round(prct, 2)), size = 3, color = "black")

hm_single <- ggplot(var_single, aes(x = variable, y = cohort_id, fill = prct)) + 
    geom_tile() +  theme_classic() +
    theme(axis.text.x=element_text(angle=90, hjust=1), axis.line=element_blank())+
    scale_fill_gradient(low = "yellow", high = "red", na.value = "lightgray", limits = c(0,100))+ labs(y = "cohort") +
    geom_text(aes(label=round(prct, 2)), size = 3, color = "black") 

plot_grid(hm_cohort, hm_single, align = "v", nrow = 2, rel_heights = c(1/2, 1/2))

## [1] "Cases with neither PCR nor serology: 13"
## [1] "Cases with neither PCR nor serology nor closecontact: 9"

4.8 Lab values

For lab values, sometimes multiple values are reported (baseline, peak or not-specified). All lab values are collapsed based on the max (or the min for e.g. hemoglobin): so only the highest value of median, Q1 or Q3 is used.

4.8.1 C-reactive protein

## [1] "Column extracted from cohorts:"
##  [1] "lab_CRP_baseline_n"   "lab_CRP_baseline_med" "lab_CRP_baseline_Q1" 
##  [4] "lab_CRP_baseline_Q3"  "lab_CRP_peak_n"       "lab_CRP_peak_med"    
##  [7] "lab_CRP_peak_Q1"      "lab_CRP_peak_Q3"      "lab_CRP_NS_n"        
## [10] "lab_CRP_NS_med"       "lab_CRP_NS_Q1"        "lab_CRP_NS_Q3"       
## [13] "cohort_id"            "cohort_type"          "tot_cases_n"
## [1] "Column extracted from single cases:"
## [1] "lab_CRP_admis" "lab_CRP_NS"    "lab_CRP_peak"

4.8.2 Lymphocytes

## [1] "Column extracted from cohorts:"
## [1] "lab_lympho_NS_n"         "lab_lympho_NS_med"      
## [3] "lab_lympho_NS_Q1"        "lab_lympho_NS_Q3"       
## [5] "lab_lympho_baseline_n"   "lab_lympho_baseline_med"
## [7] "cohort_id"               "cohort_type"            
## [9] "tot_cases_n"
## [1] "Column extracted from single cases:"
## [1] "lab_lymphocytes_lowest"

4.8.3 White blood cells

## [1] "Column extracted from cohorts:"
## [1] "lab_WBC_NS_n"         "lab_WBC_NS_med"       "lab_WBC_NS_Q1"       
## [4] "lab_WBC_NS_Q3"        "lab_WBC_baseline_n"   "lab_WBC_baseline_med"
## [7] "cohort_id"            "cohort_type"          "tot_cases_n"
## [1] "Column extracted from single cases:"
## [1] "lab_WBC_highest"

4.8.4 Ferritin

## [1] "Column extracted from cohorts:"
##  [1] "lab_ferritin_NS_n"         "lab_ferritin_NS_med"      
##  [3] "lab_ferritin_NS_Q1"        "lab_ferritin_NS_Q3"       
##  [5] "lab_ferritin_baseline_n"   "lab_ferritin_baseline_med"
##  [7] "lab_ferritin_peak_n"       "lab_ferritin_peak_med"    
##  [9] "lab_ferritin_peak_Q1"      "lab_ferritin_peak_Q3"     
## [11] "cohort_id"                 "cohort_type"              
## [13] "tot_cases_n"
## [1] "Column extracted from single cases:"
## [1] "lab_ferritin_NS"    "lab_ferritin_admis" "lab_ferritin_peak"

4.8.5 Troponin

## [1] "Column extracted from cohorts:"
##  [1] "lab_troponin_baseline_n"   "lab_troponin_baseline_med"
##  [3] "lab_troponin_baseline_Q1"  "lab_troponin_baseline_Q3" 
##  [5] "lab_troponin_peak_n"       "lab_troponin_peak_med"    
##  [7] "lab_troponin_peak_Q1"      "lab_troponin_peak_Q3"     
##  [9] "lab_troponin_NS_n"         "lab_troponin_NS_med"      
## [11] "lab_troponin_NS_Q1"        "lab_troponin_NS_Q3"       
## [13] "cohort_id"                 "cohort_type"              
## [15] "tot_cases_n"
## [1] "Column extracted from single cases:"
## [1] "lab_troponin_admis" "lab_troponin_max"

4.8.6 IL-6

Note: The cases from Pouletty et al are added to the single cases as they report on IL6 values.

## [1] "Column extracted from cohorts:"
##  [1] "lab_IL6_baseline_n"   "lab_IL6_baseline_med" "lab_IL6_baseline_Q1" 
##  [4] "lab_IL6_baseline_Q3"  "lab_IL6_NS_n"         "lab_IL6_NS_med"      
##  [7] "lab_IL6_NS_Q1"        "lab_IL6_NS_Q3"        "cohort_id"           
## [10] "cohort_type"          "tot_cases_n"
## [1] "Column extracted from single cases:"
## [1] "lab_IL6"

4.8.7 BNP

## [1] "Column extracted from cohorts:"
##  [1] "lab_BNP_baseline_n"   "lab_BNP_baseline_med" "lab_BNP_baseline_Q1" 
##  [4] "lab_BNP_baseline_Q3"  "lab_BNP_peak_n"       "lab_BNP_peak_med"    
##  [7] "lab_BNP_peak_Q1"      "lab_BNP_peak_Q3"      "lab_BNP_NS_n"        
## [10] "lab_BNP_NS_med"       "lab_BNP_NS_min"       "lab_BNP_NS_max"      
## [13] "cohort_id"            "cohort_type"          "tot_cases_n"
## [1] "Column extracted from single cases:"
## [1] "lab_BNP_admis" "lab_BNP_max"

4.8.8 NTproBNP

## [1] "Column extracted from cohorts:"
##  [1] "lab_NTproBNP_baseline_n"   "lab_NTproBNP_baseline_med"
##  [3] "lab_NTproBNP_baseline_Q1"  "lab_NTproBNP_baseline_Q3" 
##  [5] "lab_NTproBNP_peak_n"       "lab_NTproBNP_peak_med"    
##  [7] "lab_NTproBNP_peak_Q1"      "lab_NTproBNP_peak_Q3"     
##  [9] "lab_NTproBNP_NS_n"         "lab_NTproBNP_NS_med"      
## [11] "lab_NTproBNP_NS_Q1"        "lab_NTproBNP_NS_Q3"       
## [13] "cohort_id"                 "cohort_type"              
## [15] "tot_cases_n"
## [1] "Column extracted from single cases:"
## [1] "lab_NTproBNP"

4.8.9 Platelets

## [1] "Column extracted from cohorts:"
##  [1] "lab_platelet_NS_n"         "lab_platelet_NS_med"      
##  [3] "lab_platelet_NS_Q1"        "lab_platelet_NS_Q3"       
##  [5] "lab_platelet_baseline_n"   "lab_platelet_baseline_med"
##  [7] "lab_platelet_lowest_n"     "lab_platelet_lowest_med"  
##  [9] "lab_platelet_lowest_Q1"    "lab_platelet_lowest_Q3"   
## [11] "cohort_id"                 "cohort_type"              
## [13] "tot_cases_n"
## [1] "Column extracted from single cases:"
## [1] "lab_platelets_NS"      "lab_platelets_highest" "lab_platelets_lowest"

4.8.10 D-dimers

## [1] "Column extracted from cohorts:"
##  [1] "lab_Ddim_baseline_n"   "lab_Ddim_baseline_med" "lab_Ddim_baseline_Q1" 
##  [4] "lab_Ddim_baseline_Q3"  "lab_Ddim_NS_n"         "lab_Ddim_NS_med"      
##  [7] "lab_Ddim_NS_Q1"        "lab_Ddim_NS_Q3"        "lab_Ddim_peak_n"      
## [10] "lab_Ddim_peak_med"     "lab_Ddim_peak_Q1"      "lab_Ddim_peak_Q3"     
## [13] "cohort_id"             "cohort_type"           "tot_cases_n"
## [1] "Column extracted from single cases:"
## [1] "lab_Ddim_NS"   "lab_Ddim_peak"

4.8.11 Sodium

## [1] "Column extracted from cohorts:"
## [1] "lab_sodium_NS_n"         "lab_sodium_NS_med"      
## [3] "lab_sodium_NS_Q1"        "lab_sodium_NS_Q3"       
## [5] "lab_sodium_baseline_n"   "lab_sodium_baseline_med"
## [7] "cohort_id"               "cohort_type"            
## [9] "tot_cases_n"
## [1] "Column extracted from single cases:"
## [1] "lab_sodium"

5 Case definitions

5.1 Lab reference values

Cut-offs in this study:

  • Neutrophilia > 8000/µL
  • Elevated CRP > 10 mg/L
  • Lymphopenia < 1250/µL
  • WBC > 11000/µL
  • Fibrinogen > 400 mg/dL
  • D-dimers > 250 ng/mL
  • Ferritin > 300 ng/mL
  • Albumin < 34 g/L
  • Procalcitonin > 0.49 ng/mL
  • LDH > 280 U/L
  • IL6 > 16.4 pg/mL
  • ESR > 22 mm/
  • BNP > 100 pg/mL
  • NTproBNP > 400 pg/mL
  • Troponin > 0.04 ng/mL

5.2 PIMS-TS

Source RCPCH

  1. A child presenting with persistent fever, inflammation (neutrophilia, elevated CRP and lymphopaenia) and evidence of single or multi-organ dysfunction (shock, cardiac, respiratory, renal, gastrointestinal or neurological disorder) with additional features (see listed in Appendix 1 ). This may include children fulfilling full or partial criteria for Kawasaki disease.
  2. Exclusion of any other microbial cause, including bacterial sepsis, staphylococcal or streptococcal shock syndromes, infections associated with myocarditis such as enterovirus (waiting for results of these investigations should not delay seeking expert advice).
  3. SARS-CoV-2 PCR testing may be positive or negative

We are unable to evaluate criteria 2.

PIMS_TS_fulfilled <- apply(df_singlecases, 1, function(row) {
    # persistent fever, inflammation (neutrophilia, elevated CRP and lymphopaenia) 
    pat_id <- row["patientID_int"]
    fever <- row["symp_fever"] == TRUE
    neutrophilia <- as.numeric(row["lab_neutrophils"]) > co_neutrophilia
    elevated_CRP <- (as.numeric(row["lab_CRP_admis"]) > co_CRP | as.numeric(row["lab_CRP_NS"]) > co_CRP | as.numeric(row["lab_CRP_peak"]) > co_CRP )
    lymphopenia <- as.numeric(row["lab_lymphocytes_lowest"]) < co_lympho
    inflamm <- any(fever, neutrophilia, elevated_CRP, lymphopenia)
    
    # lab values
    #fibrinogen <- row["lab_fibrino"] > co_fibrino
    #Ddimers <- row["lab_Ddim_peak"] > co_Ddim |  row["lab_Ddim_NS"] > co_Ddim
    #ferritin <- (row["lab_ferritin_NS"] > co_ferritin | row["lab_ferritin_admis"] > co_ferritin | row["lab_ferritin_peak"] > co_ferritin)
    #albumin <- row["lab_albumin_admis"] < co_albu | row["lab_albumin_lowest"] < co_albu | row["lab_albumin_NS"] < co_albu
    #lab_vals <- any(fibrinogen, Ddimers, ferritin, albumin)
    
    # single or multi-organ dysfunction (shock, cardiac, respiratory, renal, gastrointestinal or neurological disorder)
    pneumonia <- row["symp_resp_pneumonia"] == TRUE
    resp_failure <- row["symp_resp_failure"] == TRUE
    resp <- any(pneumonia, resp_failure)
    
    AKI <- row["symp_renal_AKI"] == TRUE
    RRT <- row["critcare_RRT"] == TRUE
    renal <- any(AKI, RRT)
    
    myocarditis <- row["symp_cardiovasc_myocard"] == TRUE
    pericarditis <- row["symp_cardiovasc_pericard"] == TRUE
    LVEF_under30 <- row["symp_cardiovasc_LV_less30"] == TRUE
    LVEF_30to55 <- row["symp_cardiovasc_LV_30to55"] == TRUE
    BNP <- (as.numeric(row["lab_BNP_admis"]) > co_BNP | as.numeric(row["lab_BNP_max"]) > co_BNP ) 
    NTproBNP <- as.numeric(row["lab_NTproBNP"]) > co_NTproBNP
    tropo <- as.numeric(row["lab_troponin_admis"]) > co_tropo
    shock <- row["symp_cardiovasc_shock"] == TRUE
    
    cardiovasc <- any(myocarditis, LVEF_under30, LVEF_30to55, NTproBNP, BNP, tropo, shock)
    
    rash <- row["kawasaki_exanthema"] == TRUE
    dermato <- any(rash)
    
    organ_dysfunc <- sum(resp, renal, cardiovasc, dermato, na.rm = TRUE) >= 1

    criteria_fulfilled <- (inflamm) & organ_dysfunc #&lab_vals
    #return(c(pat_id, "criteria1_inflamm" = inflamm, "criteria2_labvals" = lab_vals, "criteria3_organdysfunc" = organ_dysfunc, "criteria_fulfilled" = criteria_fulfilled))
    return(c(pat_id, "criteria1_inflamm" = inflamm, "criteria3_organdysfunc" = organ_dysfunc, "criteria_fulfilled" = criteria_fulfilled))
})

PIMS_TS_fulfilled <- PIMS_TS_fulfilled %>% t() %>% as_tibble()
PIMS_TS_fulfilled <- type_convert(PIMS_TS_fulfilled)
PIMS_TS_fulfilled_heatmap <- PIMS_TS_fulfilled
cols <- sapply(PIMS_TS_fulfilled_heatmap, is.logical)
PIMS_TS_fulfilled_heatmap[,cols] <- lapply(PIMS_TS_fulfilled_heatmap[,cols], as.numeric)
PIMS_TS_fulfilled_heatmap_melt <- PIMS_TS_fulfilled_heatmap %>% melt()
PIMS_TS_fulfilled_heatmap_melt[is.na(PIMS_TS_fulfilled_heatmap_melt)] <- 2

skim(PIMS_TS_fulfilled)
Data summary
Name PIMS_TS_fulfilled
Number of rows 95
Number of columns 4
_______________________
Column type frequency:
character 1
logical 3
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
patientID_int 0 1 9 10 0 95 0

Variable type: logical

skim_variable n_missing complete_rate mean count
criteria1_inflamm 0 1 1 TRU: 95
criteria3_organdysfunc 0 1 1 TRU: 95
criteria_fulfilled 0 1 1 TRU: 95

5.3 CDC MIS-C

Source CDC and UpToDate The case definition for MIS-C is:

  1. Age <21 years
  2. Clinical presentation consistent with MIS-C, including all of the following:
    • Fever
      • Documented fever >38.0°C (100.4°F) for ≥24 hours or
      • Report of subjective fever lasting ≥24 hours
    • Laboratory evidence of inflammation
    • Severe illness requiring hospitalization
    • Multisystem involvement
      • 2 or more organ systems involved
        • Cardiovascular (eg, shock, elevated troponin, elevated BNP, abnormal echocardiogram, arrhythmia)
        • Respiratory (eg, pneumonia, ARDS, pulmonary embolism)
        • Renal (eg, AKI, renal failure)
        • Neurologic (eg, seizure, stroke, aseptic meningitis)
        • Hematologic (eg, coagulopathy)
        • Gastrointestinal (eg, elevated liver enzymes, diarrhea, ileus, gastrointestinal bleeding)
        • Dermatologic (eg, erythroderma, mucositis, other rash)
  3. No alternative plausible diagnoses
  4. Recent or current SARS-CoV-2 infection or exposure
    • Any of the following:
    • Positive SARS-CoV-2 RT-PCR
    • Positive serology
    • Positive antigen test
    • COVID-19 exposure within the 4 weeks prior to the onset of symptoms
CDC_fulfilled <- apply(df_singlecases, 1, function(row) {
    # criteria 1
    criteria1 = TRUE
    
    # criteria 2
    pat_id <- row["patientID_int"]
    
    # fever?
    fever <- row["symp_fever"] == TRUE | row["kawasaki_fever"] == TRUE

    inflamm <- any(fever)
    
    # lab values evidence for inflammation
    neutrophilia <- as.numeric(row["lab_neutrophils"]) > co_neutrophilia
    elevated_CRP <- (as.numeric(row["lab_CRP_admis"]) > co_CRP | as.numeric(row["lab_CRP_NS"]) > co_CRP | as.numeric(row["lab_CRP_peak"]) > co_CRP )
    lymphopenia <- as.numeric(row["lab_lymphocytes_lowest"]) < co_lympho
    fibrinogen <- as.numeric(row["lab_fibrino"]) > co_fibrino
    Ddimers <- as.numeric(row["lab_Ddim_peak"]) > co_Ddim |  as.numeric(row["lab_Ddim_NS"]) > co_Ddim
    ferritin <- (as.numeric(row["lab_ferritin_NS"]) > co_ferritin | as.numeric(row["lab_ferritin_admis"]) > co_ferritin | as.numeric(row["lab_ferritin_peak"]) > co_ferritin)
    albumin <- as.numeric(row["lab_albumin_admis"]) < co_albu | as.numeric(row["lab_albumin_lowest"]) < co_albu | as.numeric(row["lab_albumin_NS"]) < co_albu
    PCT <- as.numeric(row["lab_PCT_admis"]) > co_PCT | as.numeric(row["lab_PCT_peak"]) > co_PCT | as.numeric(row["lab_PCT_NS"]) > co_PCT 
    LDH <- as.numeric(row["lab_LDH"]) > co_LDH
    IL6 <- as.numeric(row["lab_IL6"]) > co_IL6
    ESR <- as.numeric(row["lab_ESR"]) > co_ESR

    lab_vals <- any(neutrophilia, elevated_CRP, lymphopenia, fibrinogen, Ddimers, ferritin, albumin, PCT, LDH, IL6, ESR)
    
    # Ilness requiring hospitalisation
    ## used surrogate parameters for hosp
    hosp_ICU <- row["admis_hosp_days"] > 1 | row["admis_ICU_days"] > 1 | row["admis_PICU_admis"] == TRUE
    NIV <- row["critcare_NIV"] == TRUE | row["critcare_NIV_days"] > 1
    MV <- row["critcare_MV"] == TRUE | row["critcare_MV_days"] > 1
    inotrop <- row["critcare_inotrop"] == TRUE | row["critcare_inotrop_days"] > 1
    ECMO <- row["critcare_ECMO"] == TRUE 
    IVIg <- row["rx_IVIg_once"] == TRUE  |  row["rx_IVIg_multip"] == TRUE 
    biologicals <- row["rx_anakinra"] == TRUE | row["rx_tocilizumab"] == TRUE | row["rx_infliximab"] == TRUE | row["rx_antibiotics"] == TRUE | row["rx_plasma"] == TRUE | row["rx_remdesivir"] == TRUE 
    heparin <- row["rx_heparin"] == TRUE

    req_hosp <- any(hosp_ICU, NIV, MV, inotrop, ECMO, IVIg, biologicals, heparin)
    
    ## multisystem involvement >= 2
    ## respiratory
    pneumonia <- row["symp_resp_pneumonia"] == TRUE
    resp_failure <- row["symp_resp_failure"] == TRUE
    resp <- any(pneumonia, resp_failure)
    
    AKI <- row["symp_renal_AKI"] == TRUE
    RRT <- row["critcare_RRT"] == TRUE
    renal <- any(AKI, RRT)
    
    myocarditis <- row["symp_cardiovasc_myocard"] == TRUE
    pericarditis <- row["symp_cardiovasc_pericard"] == TRUE
    LVEF_under30 <- row["symp_cardiovasc_LV_less30"] == TRUE
    LVEF_30to55 <- row["symp_cardiovasc_LV_30to55"] == TRUE
    BNP <- (as.numeric(row["lab_BNP_admis"]) > co_BNP | as.numeric(row["lab_BNP_max"]) > co_BNP ) 
    NTproBNP <- as.numeric(row["lab_NTproBNP"]) > co_NTproBNP
    tropo <- as.numeric(row["lab_troponin_admis"]) > co_tropo
    shock <- row["symp_cardiovasc_shock"] == TRUE
    
    cardiovasc <- any(myocarditis, LVEF_under30, LVEF_30to55, NTproBNP, BNP, tropo, shock)
    
    rash <- row["kawasaki_exanthema"] == TRUE
    dermato <- any(rash)
    
    organ_dysfunc <- sum(resp, renal, cardiovasc, dermato, na.rm = TRUE) >= 2
    
    criteria2 <- sum(inflamm, lab_vals, req_hosp, organ_dysfunc, na.rm = TRUE) == 4
    # criteria 3
    ## not evaluable
    criteria3 = TRUE
    # criteria 4
    # COVID pos?
    PCR_pos <- row["covid_PCR_pos"] == TRUE
    stool_pos <- row["covid_PCR_stool_pos"] == TRUE
    closecontact <- row["covid_closecontact"] == TRUE
    IgA <- row["covid_IgA_pos"] == TRUE
    IgM <- row["covid_IgM_pos"] == TRUE    
    IgG <- row["covid_IgG_pos"] == TRUE    
    any_sero <- row["covid_sero_pos"] == TRUE
    
    criteria4 <- any(PCR_pos, stool_pos, closecontact, IgA, IgM, IgG, any_sero)
    
    if (FALSE %in% c(criteria1, criteria2, criteria3, criteria4)){
      criteria_fulfilled <- FALSE
    } else if (NA %in% c(criteria1, criteria2, criteria3, criteria4)){
      criteria_fulfilled <- NA
    } else if (sum(criteria1, criteria2, criteria3, criteria4, na.rm = TRUE) == 4){
      criteria_fulfilled <- TRUE
    }
    
    #criteria_fulfilled <- sum(criteria1, criteria2, criteria3, criteria4, na.rm = TRUE) == 4
    return(c(pat_id, "criteria1_age" = criteria1, "criteria2_clinical" = criteria2, "criteria3_noAlt" = criteria3, "criteria4_recentExposure" = criteria4, "criteria_fulfilled" = criteria_fulfilled))
})

CDC_fulfilled <- CDC_fulfilled %>% t() %>% as_tibble()
CDC_fulfilled <- type_convert(CDC_fulfilled)
CDC_fulfilled_heatmap <- CDC_fulfilled
cols <- sapply(CDC_fulfilled_heatmap, is.logical)
CDC_fulfilled_heatmap[,cols] <- lapply(CDC_fulfilled_heatmap[,cols], as.numeric)
CDC_fulfilled_heatmap_melt <- CDC_fulfilled_heatmap %>% melt()
CDC_fulfilled_heatmap_melt[is.na(CDC_fulfilled_heatmap_melt)] <- 2

skim(CDC_fulfilled)
Data summary
Name CDC_fulfilled
Number of rows 95
Number of columns 6
_______________________
Column type frequency:
character 1
logical 5
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
patientID_int 0 1 9 10 0 95 0

Variable type: logical

skim_variable n_missing complete_rate mean count
criteria1_age 0 1.00 1.00 TRU: 95
criteria2_clinical 0 1.00 0.69 TRU: 66, FAL: 29
criteria3_noAlt 0 1.00 1.00 TRU: 95
criteria4_recentExposure 9 0.91 1.00 TRU: 86
criteria_fulfilled 7 0.93 0.67 TRU: 59, FAL: 29

5.4 WHO case definition

Source UpToDate:

All 6 criteria must be met:

  1. Age 0 to 19 years
  2. Fever for ≥3 days
  3. Clinical signs of multisystem involvement (at least 2 of the following):
    • Rash, bilateral nonpurulent conjunctivitis, or mucocutaneous inflammation signs (oral, hands, or feet)
    • Hypotension or shock
    • Cardiac dysfunction, pericarditis, valvulitis, or coronary abnormalities (including echocardiographic findings or elevated troponin/BNP)
    • Evidence of coagulopathy (prolonged PT or PTT; elevated D-dimer)
    • Acute gastrointestinal symptoms (diarrhea, vomiting, or abdominal pain)
  4. Elevated markers of inflammation (eg, ESR, CRP, or procalcitonin)
  5. No other obvious microbial cause of inflammation, including bacterial sepsis and staphylococcal/streptococcal toxic shock syndromes
  6. Evidence of SARS-CoV-2 infection
    • Any of the following:
    • Positive SARS-CoV-2 RT-PCR
    • Positive serology
    • Positive antigen test
    • Contact with an individual with COVID-19
#row <- df_singlecases[87, ]
WHO_fulfilled <- apply(df_singlecases, 1, function(row) {
    pat_id <- row["patientID_int"]
    
    # criteria 1
    criteria1 = TRUE
    
    # criteria 2: fever?
    fever <- row["symp_fever"] == TRUE | row["kawasaki_fever"] == TRUE

    criteria2 <- any(fever)
    
    # criteria 3: clinical signs of multisystem involvement (at least 2)
    ## Rash, bilateral nonpurulent conjunctivitis, or mucocutaneous inflammation signs (oral, hands, or feet)
    rash <- row["kawasaki_exanthema"] == TRUE
    conjunctivitis <- row["kawasaki_conjunctivitis"] == TRUE
    mucocutaneaous <- row["kawasaki_mouth"] == TRUE | row["kawasaki_extremity"] == TRUE
    
    criteria3_a <- any(rash, conjunctivitis, mucocutaneaous)
    
    ## hypotension or shock
    shock <- row["symp_cardiovasc_shock"] == TRUE
    criteria3_b <- any(shock)
    
    ## cardiac dysfunction
    myocarditis <- row["symp_cardiovasc_myocard"] == TRUE
    pericarditis <- row["symp_cardiovasc_pericard"] == TRUE
    LVEF_under30 <- row["symp_cardiovasc_LV_less30"] == TRUE
    LVEF_30to55 <- row["symp_cardiovasc_LV_30to55"] == TRUE
    BNP <- (as.numeric(row["lab_BNP_admis"]) > co_BNP | as.numeric(row["lab_BNP_max"]) > co_BNP ) 
    NTproBNP <- as.numeric(row["lab_NTproBNP"]) > co_NTproBNP
    tropo <- as.numeric(row["lab_troponin_admis"]) > co_tropo
    coronary <- row["symp_cardiovasc_cordilat"] == TRUE | row["symp_cardiovasc_aneurysm"] == TRUE
    
    criteria3_c <- any(myocarditis, LVEF_under30, LVEF_30to55, NTproBNP, BNP, tropo, coronary)
    
    ## coagulopathy
    fibrinogen <- as.numeric(row["lab_fibrino"]) > co_fibrino
    Ddimers <- as.numeric(row["lab_Ddim_peak"]) > co_Ddim |  as.numeric(row["lab_Ddim_NS"]) > co_Ddim
    
    criteria3_d <- any(fibrinogen, Ddimers)
    
    ## acute GI symptoms
    GIsymp <- row["symp_GI_NS"] == TRUE | row["symp_GI_abdopain"] == TRUE | row["symp_GI_vomiting"] == TRUE | row["symp_GI_diarrh"] == TRUE | row["symp_GI_colitis"] == TRUE 
    
    criteria3_e <- any(GIsymp)
    
    criteria3 <- sum(criteria3_a, criteria3_b, criteria3_c, criteria3_d, criteria3_e, na.rm = TRUE) >= 2
      
    # criteria 4: Elevated markers of inflammation (eg, ESR, CRP, or procalcitonin)
    neutrophilia <- as.numeric(row["lab_neutrophils"]) > co_neutrophilia
    elevated_CRP <- (as.numeric(row["lab_CRP_admis"]) >= co_CRP) | (as.numeric(row["lab_CRP_NS"]) >= co_CRP) | (as.numeric(row["lab_CRP_peak"]) >= co_CRP )
  #  print(paste0(pat_id, elevated_CRP, row["lab_CRP_peak"]))
    lymphopenia <- as.numeric(row["lab_lymphocytes_lowest"]) < co_lympho

    ferritin <- (as.numeric(row["lab_ferritin_NS"]) > co_ferritin | as.numeric(row["lab_ferritin_admis"]) > co_ferritin | as.numeric(row["lab_ferritin_peak"]) > co_ferritin)
    albumin <- as.numeric(row["lab_albumin_admis"]) < co_albu | as.numeric(row["lab_albumin_lowest"]) < co_albu | as.numeric(row["lab_albumin_NS"]) < co_albu
    PCT <- as.numeric(row["lab_PCT_admis"]) > co_PCT | as.numeric(row["lab_PCT_peak"]) > co_PCT | as.numeric(row["lab_PCT_NS"]) > co_PCT 
    LDH <- as.numeric(row["lab_LDH"]) > co_LDH
    IL6 <- as.numeric(row["lab_IL6"]) > co_IL6
    ESR <- as.numeric(row["lab_ESR"]) > co_ESR

    criteria4 <- any(neutrophilia, elevated_CRP, lymphopenia, ferritin, albumin, PCT, LDH, IL6, ESR)

    # criteria 5: No other obvious microbial cause of inflammation
    criteria5 <- TRUE
    
    # criteria 6: COVID pos?
    PCR_pos <- row["covid_PCR_pos"] == TRUE
    stool_pos <- row["covid_PCR_stool_pos"] == TRUE
    closecontact <- row["covid_closecontact"] == TRUE
    IgA <- row["covid_IgA_pos"] == TRUE
    IgM <- row["covid_IgM_pos"] == TRUE    
    IgG <- row["covid_IgG_pos"] == TRUE    
    any_sero <- row["covid_sero_pos"] == TRUE
    
    criteria6 <- any(PCR_pos, stool_pos, closecontact, IgA, IgM, IgG, any_sero)
    
    if (NA %in% c(criteria1, criteria2, criteria3, criteria4, criteria5, criteria6)){
      criteria_fulfilled <- NA
    } else if (FALSE %in% c(criteria1, criteria2, criteria3, criteria4, criteria5, criteria6)){
      criteria_fulfilled <- FALSE
    } else if (sum(criteria1, criteria2, criteria3, criteria4, criteria5, criteria6, na.rm = TRUE) == 6){
      criteria_fulfilled <- TRUE
    } else {
      criteria_fulfilled <- FALSE
    }

    return(c(pat_id, "criteria1_age" = criteria1, "criteria2_fever" = criteria2, "criteria3_clinical" = criteria3, "criteria4_inflamm" = criteria4, "criteria5_noAlt" = criteria5, "criteria6_recentExposure" = criteria6, "criteria_fulfilled" = criteria_fulfilled))
})


WHO_fulfilled <- WHO_fulfilled %>% t() %>% as_tibble()
WHO_fulfilled <- type_convert(WHO_fulfilled)
WHO_fulfilled_heatmap <- WHO_fulfilled
cols <- sapply(WHO_fulfilled_heatmap, is.logical)
WHO_fulfilled_heatmap[,cols] <- lapply(WHO_fulfilled_heatmap[,cols], as.numeric)
WHO_fulfilled_heatmap_melt <- WHO_fulfilled_heatmap %>% melt()
WHO_fulfilled_heatmap_melt[is.na(WHO_fulfilled_heatmap_melt)] <- 2

skim(WHO_fulfilled)
Data summary
Name WHO_fulfilled
Number of rows 95
Number of columns 8
_______________________
Column type frequency:
character 1
logical 7
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
patientID_int 0 1 9 10 0 95 0

Variable type: logical

skim_variable n_missing complete_rate mean count
criteria1_age 0 1.00 1.00 TRU: 95
criteria2_fever 0 1.00 1.00 TRU: 95
criteria3_clinical 0 1.00 0.98 TRU: 93, FAL: 2
criteria4_inflamm 0 1.00 1.00 TRU: 95
criteria5_noAlt 0 1.00 1.00 TRU: 95
criteria6_recentExposure 9 0.91 1.00 TRU: 86
criteria_fulfilled 9 0.91 0.98 TRU: 84, FAL: 2

5.6 Summary

criteria_summary <- data.frame(PIMS_TS_fulfilled %>% select(criteria_fulfilled), CDC_fulfilled %>% select(criteria_fulfilled), WHO_fulfilled %>% select(criteria_fulfilled))
colnames(criteria_summary) <- c("PIMS-TS", "CDC", "WHO")

cols <- sapply(criteria_summary, is.logical)
criteria_summary[,cols] <- lapply(criteria_summary[,cols], as.numeric)

criteria_summary <- criteria_summary %>% melt() %>% 
                          group_by(variable) %>% 
                          summarise(fulfilled = sum(value == 1, na.rm = TRUE), not_fulfilled = sum(value == 0, na.rm = TRUE), not_evaluable = sum(is.na(value)))
criteria_summary$sum <- rowSums(criteria_summary[,-1])

criteria_summary_melt <- criteria_summary %>% melt()
colnames(criteria_summary_melt) <- c("center", "fulfilled", "count")

fill_bar <- ggplot(criteria_summary_melt %>% filter(fulfilled != 'sum'), aes(x = center, y = count, fill = fulfilled)) + 
      geom_bar(stat = "identity", position = "fill") + theme_bw() + 
      labs(y = "ratio", title = "Single cases meeting which criteria", subtitle = paste0("percent of total (n = ", max(criteria_summary_melt$count) ,")")) +
        scale_fill_manual(values = wes_palette("Royal1")[c(1,2,4)])

dodge_bar <- ggplot(criteria_summary_melt %>% filter(fulfilled != 'sum'), aes(x = center, y = count, fill = fulfilled)) + 
      geom_bar(stat = "identity", position = "dodge") + theme_bw() + 
      labs(y = "n", title = "Single cases meeting which criteria", subtitle = "absolute values") +
        scale_fill_manual(values = wes_palette("Royal1")[c(1,2,4)])

ggarrange(dodge_bar, fill_bar, legend = "bottom", common.legend = TRUE)

6 Association of case definition with outcome

A new variable ‘unfavourable course’ made, which contains the following:

  • symp_cardiovasc_cordilat
  • symp_cardiovasc_aneurysm
  • symp_cardiovasc_shock
  • outcome_death
  • critcare_MV
  • critcare_ECMO
  • critcare_RRT
  • critcare_inotrop
  • admis_PICU_admis

Mild presentation means all of the above are either 0 or NA.

A new variable ‘PICU candidate’ made, which contains the following:

  • symp_cardiovasc_shock
  • outcome_death
  • critcare_MV
  • critcare_ECMO
  • critcare_RRT
  • critcare_inotrop
  • admis_PICU_admis

Mild presentation means all of the above are either 0 or NA.

7 Final figures

7.1 Sex

var_cohort <- df_cohort %>% select(contains("sex") | ("cohort_id") | "tot_cases_n")
var_cohort$cohort_id <- paste0(var_cohort$cohort_id, " (n = ", var_cohort$tot_cases_n,")")
sex_f <- var_cohort %>% group_by(cohort_id) %>% summarize(prct = sex_f/tot_cases_n) %>%  mutate(sex = "female")
sex_m <- var_cohort %>% group_by(cohort_id) %>% summarize(prct = sex_m/tot_cases_n) %>% mutate(sex = "male")
sex_all <- rbind(sex_f, sex_m)

p_sex_cohort <- ggplot(sex_all, aes(y = cohort_id, x = prct, fill = sex)) + 
  geom_bar(stat = "identity", position = "fill") + 
  theme_bw() + labs(x = "") +  labs(y = "") +
  scale_fill_manual(values = wes_palette("Royal1")) + theme(legend.position = "top", legend.title=element_blank())+
  rremove("y.text") 

var_controls <- df_cohort_controls %>% filter(cohort_type == "control") %>% select(contains("sex") | ("cohort_id") | "tot_cases_n")
var_controls$cohort_id <- paste0(var_controls$cohort_id, " (n = ", var_controls$tot_cases_n,")")
sex_f <- var_controls %>% group_by(cohort_id) %>% summarize(prct = sex_f/tot_cases_n) %>% mutate(sex = "female")
sex_m <- var_controls %>% group_by(cohort_id) %>% summarize(prct = sex_m/tot_cases_n) %>% mutate(sex = "male")
sex_all <- rbind(sex_f, sex_m)

p_sex_controls <- ggplot(sex_all, aes(y = cohort_id, x = prct, fill = sex)) + 
  geom_bar(stat = "identity", position = "fill") + 
  theme_bw() + labs(x = "") + 
  scale_fill_manual(values = wes_palette("Royal1"))+
  theme(legend.position = "none")  + labs(y = "")+
  rremove("y.text") 

n_single <- df_singlecases %>% nrow()
var_single <- df_singlecases %>% select(contains("sex"))
var_single$sex_m <- ifelse(var_single$sex == "M", TRUE, FALSE)
var_single$sex_f <- ifelse(var_single$sex == "F", TRUE, FALSE)
cols <- sapply(var_single, is.logical)
var_single[,cols] <- lapply(var_single[,cols], as.numeric)
var_single <- colSums(var_single %>% select(-sex), na.rm = TRUE)
var_single <- var_single/nrow(df_singlecases)*100

sex_single <- data.frame(cohort_id = paste0("single cases (n = ", n_single_cases, ")"), prct = c(var_single["sex_m"], var_single["sex_f"]), sex = c("male", "female"))

p_sex_single <- ggplot(sex_single, aes(y = cohort_id, x = prct, fill = sex)) + 
  geom_bar(stat = "identity", position = "fill") + 
  theme_bw() + 
  scale_fill_manual(values = wes_palette("Royal1"))+
  theme(legend.position = "none") + labs(y = "", x = "Fraction")+ rremove("y.text") 

plot_sex <- plot_grid(p_sex_cohort, p_sex_controls, p_sex_single, align = "v", nrow = 3, rel_heights = c(2/3, 1/5, 1/3))
plot_sex

7.2 Age distribution

cohort_age <- df_cohort_controls %>% select(contains("cohort_id") | contains("age") | contains("cohort_type")  | contains("tot_cases_n"))
cohort_age$cohort_id <- paste0(cohort_age$cohort_id, " (n = ", cohort_age$tot_cases_n,")")
cohort_age$age_med_yrs <- as.numeric(cohort_age$age_med_yrs )
cohort_age$age_Q1_yrs <- as.numeric(cohort_age$age_Q1_yrs)
cohort_age$age_Q3_yrs <- as.numeric(cohort_age$age_Q3_yrs)
cohort_age$age_min_yrs <- as.numeric(cohort_age$age_min_yrs)
cohort_age$age_max_yrs <- as.numeric(cohort_age$age_max_yrs)

cohort_age$data_descr <- ifelse(!is.na(cohort_age$age_Q1_yrs) & is.na(cohort_age$age_min_yrs) , "IQR", 
                                ifelse(is.na(cohort_age$age_Q1_yrs) & !is.na(cohort_age$age_min_yrs), "range", 
                                       ifelse(!is.na(cohort_age$age_Q1_yrs) & !is.na(cohort_age$age_min_yrs), "IQR + range", "none")))

p_age_cohort <- ggplot(cohort_age %>% filter(cohort_type == "covid"), aes(y = cohort_id, x = age_med_yrs, col = data_descr)) + 
  geom_point(size = 4) + 
  geom_errorbar(aes(xmin=age_Q1_yrs, xmax=age_Q3_yrs), width=.8, position=position_dodge(.9)) +
  geom_errorbar(aes(xmin=age_min_yrs,  xmax=age_max_yrs), width=.2, position=position_dodge(.9)) +
  theme_bw() + lims(x = c(0,21)) + 
  labs(y = "", x = "", col = "bars") + theme(legend.position="top", legend.title=element_blank())+
  scale_color_manual(values = c(wes_palette("BottleRocket2")[1:3], wes_palette("BottleRocket1")[2]))

p_age_controls <- ggplot(cohort_age %>% filter(cohort_type != "covid"), aes(y = cohort_id, x = age_med_yrs, col = data_descr)) + 
  geom_point(size = 4) + 
  geom_errorbar(aes(xmin=age_Q1_yrs, xmax=age_Q3_yrs), width=.2, position=position_dodge(.9)) +
  geom_errorbar(aes(xmin=age_min_yrs,  xmax=age_max_yrs), width=.2, position=position_dodge(.9)) +
  theme_bw() + lims(x = c(0,21)) +
  labs(y = "", x = "", col = "bars") + theme(legend.position="none")+
  scale_color_manual(values = wes_palette("BottleRocket2")[1])

p_age_single <- ggplot(df_singlecases, aes(x = as.numeric(age), y = paste0("single cases (n = ", n_single,")"))) +
  geom_violin(fill = wes_palette("Darjeeling2")[4]) + 
  geom_boxplot(width=.3, fill = wes_palette("Darjeeling2")[1]) + 
  theme_bw() + geom_beeswarm(groupOnX=FALSE, alpha = 0.5) + lims(x = c(0,21)) + 
  labs(y = "", x = "Age (years)")

plot_age <- plot_grid(p_age_cohort, p_age_controls, p_age_single, align = "v", nrow = 3, rel_heights = c(2/3, 1/5, 1/3))
plot_age

7.3 Lab values

7.3.1 C-reactive protein

## [1] "Column extracted from cohorts:"
##  [1] "lab_CRP_baseline_n"   "lab_CRP_baseline_med" "lab_CRP_baseline_Q1" 
##  [4] "lab_CRP_baseline_Q3"  "lab_CRP_peak_n"       "lab_CRP_peak_med"    
##  [7] "lab_CRP_peak_Q1"      "lab_CRP_peak_Q3"      "lab_CRP_NS_n"        
## [10] "lab_CRP_NS_med"       "lab_CRP_NS_Q1"        "lab_CRP_NS_Q3"       
## [13] "cohort_id"            "cohort_type"          "tot_cases_n"
## [1] "Column extracted from single cases:"
## [1] "lab_CRP_admis" "lab_CRP_NS"    "lab_CRP_peak"

7.3.2 Ferritin

## [1] "Column extracted from cohorts:"
##  [1] "lab_ferritin_NS_n"         "lab_ferritin_NS_med"      
##  [3] "lab_ferritin_NS_Q1"        "lab_ferritin_NS_Q3"       
##  [5] "lab_ferritin_baseline_n"   "lab_ferritin_baseline_med"
##  [7] "lab_ferritin_peak_n"       "lab_ferritin_peak_med"    
##  [9] "lab_ferritin_peak_Q1"      "lab_ferritin_peak_Q3"     
## [11] "cohort_id"                 "cohort_type"              
## [13] "tot_cases_n"
## [1] "Column extracted from single cases:"
## [1] "lab_ferritin_NS"    "lab_ferritin_admis" "lab_ferritin_peak"

7.3.3 IL-6

Note: The cases from Pouletty et al are added to the single cases as they report on IL6 values.

## [1] "Column extracted from cohorts:"
##  [1] "lab_IL6_baseline_n"   "lab_IL6_baseline_med" "lab_IL6_baseline_Q1" 
##  [4] "lab_IL6_baseline_Q3"  "lab_IL6_NS_n"         "lab_IL6_NS_med"      
##  [7] "lab_IL6_NS_Q1"        "lab_IL6_NS_Q3"        "cohort_id"           
## [10] "cohort_type"          "tot_cases_n"
## [1] "Column extracted from single cases:"
## [1] "lab_IL6"

7.3.4 White blood cells

## [1] "Column extracted from cohorts:"
## [1] "lab_WBC_NS_n"         "lab_WBC_NS_med"       "lab_WBC_NS_Q1"       
## [4] "lab_WBC_NS_Q3"        "lab_WBC_baseline_n"   "lab_WBC_baseline_med"
## [7] "cohort_id"            "cohort_type"          "tot_cases_n"
## [1] "Column extracted from single cases:"
## [1] "lab_WBC_highest"

7.3.5 Lymphocytes

## [1] "Column extracted from cohorts:"
## [1] "lab_lympho_NS_n"         "lab_lympho_NS_med"      
## [3] "lab_lympho_NS_Q1"        "lab_lympho_NS_Q3"       
## [5] "lab_lympho_baseline_n"   "lab_lympho_baseline_med"
## [7] "cohort_id"               "cohort_type"            
## [9] "tot_cases_n"
## [1] "Column extracted from single cases:"
## [1] "lab_lymphocytes_lowest"

7.3.6 Troponin

## [1] "Column extracted from cohorts:"
##  [1] "lab_troponin_baseline_n"   "lab_troponin_baseline_med"
##  [3] "lab_troponin_baseline_Q1"  "lab_troponin_baseline_Q3" 
##  [5] "lab_troponin_peak_n"       "lab_troponin_peak_med"    
##  [7] "lab_troponin_peak_Q1"      "lab_troponin_peak_Q3"     
##  [9] "lab_troponin_NS_n"         "lab_troponin_NS_med"      
## [11] "lab_troponin_NS_Q1"        "lab_troponin_NS_Q3"       
## [13] "cohort_id"                 "cohort_type"              
## [15] "tot_cases_n"
## [1] "Column extracted from single cases:"
## [1] "lab_troponin_admis" "lab_troponin_max"

7.3.7 Platelets

## [1] "Column extracted from cohorts:"
##  [1] "lab_platelet_NS_n"         "lab_platelet_NS_med"      
##  [3] "lab_platelet_NS_Q1"        "lab_platelet_NS_Q3"       
##  [5] "lab_platelet_baseline_n"   "lab_platelet_baseline_med"
##  [7] "lab_platelet_lowest_n"     "lab_platelet_lowest_med"  
##  [9] "lab_platelet_lowest_Q1"    "lab_platelet_lowest_Q3"   
## [11] "cohort_id"                 "cohort_type"              
## [13] "tot_cases_n"
## [1] "Column extracted from single cases:"
## [1] "lab_platelets_NS"      "lab_platelets_highest" "lab_platelets_lowest"

7.3.8 D-dimers

## [1] "Column extracted from cohorts:"
##  [1] "lab_Ddim_baseline_n"   "lab_Ddim_baseline_med" "lab_Ddim_baseline_Q1" 
##  [4] "lab_Ddim_baseline_Q3"  "lab_Ddim_NS_n"         "lab_Ddim_NS_med"      
##  [7] "lab_Ddim_NS_Q1"        "lab_Ddim_NS_Q3"        "lab_Ddim_peak_n"      
## [10] "lab_Ddim_peak_med"     "lab_Ddim_peak_Q1"      "lab_Ddim_peak_Q3"     
## [13] "cohort_id"             "cohort_type"           "tot_cases_n"
## [1] "Column extracted from single cases:"
## [1] "lab_Ddim_NS"   "lab_Ddim_peak"

7.3.9 Sodium

## [1] "Column extracted from cohorts:"
## [1] "lab_sodium_NS_n"         "lab_sodium_NS_med"      
## [3] "lab_sodium_NS_Q1"        "lab_sodium_NS_Q3"       
## [5] "lab_sodium_baseline_n"   "lab_sodium_baseline_med"
## [7] "cohort_id"               "cohort_type"            
## [9] "tot_cases_n"
## [1] "Column extracted from single cases:"
## [1] "lab_sodium"

8 SessionInfo

## R version 3.6.3 (2020-02-29)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Catalina 10.15.5
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] gdtools_0.2.1      wesanderson_0.3.6  see_0.5.1          UpSetR_1.4.0      
##  [5] skimr_2.1.1        psych_1.9.12.31    zoo_1.8-7          DT_0.13           
##  [9] naniar_0.5.2       cowplot_1.0.0      ggpubr_0.2.5       magrittr_1.5      
## [13] ggbeeswarm_0.6.0   ggExtra_0.9        gridExtra_2.3      ggrepel_0.8.2     
## [17] scales_1.1.1       RColorBrewer_1.1-2 broom_0.5.5        reshape2_1.4.3    
## [21] httr_1.4.1         readxl_1.3.1       forcats_0.5.0      stringr_1.4.0     
## [25] dplyr_0.8.5        purrr_0.3.4        readr_1.3.1        tidyr_1.0.2       
## [29] tibble_3.0.1       ggplot2_3.3.1      tidyverse_1.3.0   
## 
## loaded via a namespace (and not attached):
##  [1] nlme_3.1-144      fs_1.4.0          lubridate_1.7.4   insight_0.8.5    
##  [5] repr_1.1.0        tools_3.6.3       backports_1.1.7   R6_2.4.1         
##  [9] vipor_0.4.5       DBI_1.1.0         colorspace_1.4-1  withr_2.2.0      
## [13] tidyselect_1.1.0  mnormt_1.5-6      compiler_3.6.3    cli_2.0.2        
## [17] rvest_0.3.5       xml2_1.3.0        labeling_0.3      bayestestR_0.7.0 
## [21] ggridges_0.5.2    systemfonts_0.1.1 digest_0.6.25     svglite_1.2.3    
## [25] rmarkdown_2.1     base64enc_0.1-3   pkgconfig_2.0.3   htmltools_0.4.0  
## [29] highr_0.8         dbplyr_1.4.2      fastmap_1.0.1     htmlwidgets_1.5.1
## [33] rlang_0.4.6       rstudioapi_0.11   shiny_1.4.0.2     farver_2.0.3     
## [37] generics_0.0.2    jsonlite_1.6.1    crosstalk_1.1.0.1 parameters_0.8.0 
## [41] Rcpp_1.0.4        munsell_0.5.0     fansi_0.4.1       lifecycle_0.2.0  
## [45] visdat_0.5.3      stringi_1.4.6     yaml_2.2.1        plyr_1.8.6       
## [49] grid_3.6.3        parallel_3.6.3    promises_1.1.0    crayon_1.3.4     
## [53] miniUI_0.1.1.1    lattice_0.20-38   haven_2.2.0       hms_0.5.3        
## [57] knitr_1.28        pillar_1.4.4      ggsignif_0.6.0    effectsize_0.3.1 
## [61] reprex_0.3.0      glue_1.4.1        evaluate_0.14     modelr_0.1.6     
## [65] vctrs_0.3.0       httpuv_1.5.2      cellranger_1.1.0  gtable_0.3.0     
## [69] assertthat_0.2.1  xfun_0.12         mime_0.9          xtable_1.8-4     
## [73] later_1.0.0       beeswarm_0.2.3    ellipsis_0.3.1
---
title: "Multisystem inflammatory syndrome in children related to COVID-19: a systematic review"
subtitle: "Data analysis"
author: "Levi Hoste, Ruben Van Paemel*, Filomeen Haerynck"
date: "`r format(Sys.time(), '%d %B, %Y, %H:%M:%S')`"
output:
  html_document:
    code_folding: hide
    highlight: kate
    number_sections: yes
    theme: cosmo
    toc: yes
    toc_float: yes
    df_print: paged
    code_download: true
    fig_caption: yes
---

# Introduction
In this RMarkdown file, the data-analysis for the manuscript "Multisystem inflammatory syndrome in children related to COVID-19: a systematic review" is described. The complete data-analysis can be reproduced from the data collection sheet (in .xls format), provided in the supplementary data. 

```{r setup, include=TRUE, warning = FALSE, message = FALSE}
knitr::opts_chunk$set(cache = FALSE, warning = FALSE, message = FALSE)
options(digits = 3)
library(tidyverse)
require(readxl)
require(httr)
require(reshape2)
require(broom)
require(RColorBrewer)
require(scales)
require(ggrepel)
require(gridExtra)
require(ggExtra)
library(ggbeeswarm)
require(ggpubr)
library(cowplot)
library(naniar)
require(DT)
require(zoo)
require(psych)
library(skimr)
library(UpSetR)
library(see)
library(wesanderson)

options(scipen=999)

co_hb <- 12
co_neutrophilia <- 8000
co_CRP <- 10
co_lympho <- 1250
co_fibrino <- 400
co_Ddim <- 250
co_ferritin <- 300
co_albu <- 34
co_PCT <- 0.49
co_LDH <- 280
co_IL6 <- 16.4
co_ESR <- 22
co_BNP <- 100
co_NTproBNP <- 400
co_tropo <- 40
co_WBC <- 11000
co_platelet <- 150000
co_sodium <- 135
#input = df_cohort_controls
#find = "max"
#param = "CRP"

collapse_labvals_cohort <- function(input, find, param){
  if (find == "max"){
    df <- input %>% select(contains(param) | contains("cohort_id") | contains("cohort_type") | contains("tot_cases_n"))
    print("Column extracted from cohorts:")
    print(colnames(df))
    df_med <- df %>% select(contains("med"))
    df_med <- type_convert(df_med)
    df_med <- df_med %>% mutate_all(funs(replace_na(., -999)))
   # colnames(df_med)[max.col(df_med,ties.method="first")]
    df_med <- df_med %>% mutate(med = as.numeric(apply(df_med, 1, max)))
    
    df_min <- df %>% select(contains("Q1"))
    df_min <- type_convert(df_min)
    df_min <- df_min %>% mutate_all(funs(replace_na(., 0)))
    #colnames(df_min)[max.col(df_min,ties.method="first")]
    df_min <- df_min %>% mutate(min = as.numeric(apply(df_min, 1, max)))
    
    df_max <- df %>% select(contains("Q3"))
    df_max <- type_convert(df_max)
    df_max <- df_max %>% mutate_all(funs(replace_na(., 0)))
    #colnames(df_max)[max.col(df_max,ties.method="first")]
    df_max <- df_max %>% mutate(max = as.numeric(apply(df_max, 1, max)))
    
    df_full <- cbind(df %>% select(cohort_id, cohort_type, tot_cases_n), df_med %>% select(med), df_min %>% select(min), df_max %>% select(max))
    df_full[df_full == -999] <- NA
    names(df_full)[names(df_full) == 'max'] <- paste0(param, "_max")
    names(df_full)[names(df_full) == 'min'] <- paste0(param, "_min")
    names(df_full)[names(df_full) == 'med'] <- paste0(param, "_med")
    df_full$data_descr <- "IQR"
    df_full$cohort_id <- paste0(df_full$cohort_id, " (n = ", as.character(df_full$tot_cases_n),")")
    write.csv(df_full, paste0("./data/cohort_", param, ".csv"))
    print(datatable(df_full, caption = paste0("overview of ", param)))
    return(df_full)
  }
    else if (find == "min"){
    df <- input %>% select(contains(param) | contains("cohort_id") | contains("cohort_type") | contains("tot_cases_n"))
    print("Column extracted from cohorts:")
    print(colnames(df))
    df_med <- df %>% select(contains("med"))
    df_med <- type_convert(df_med)
    df_med <- df_med %>% mutate_all(funs(replace_na(., 1e6)))
   # colnames(df_med)[max.col(df_med,ties.method="first")]
    df_med <- df_med %>% mutate(med = as.numeric(apply(df_med, 1, min)))
    
    df_min <- df %>% select(contains("Q1"))
    df_min <- type_convert(df_min)
    df_min <- df_min %>% mutate_all(funs(replace_na(., 1e6)))
    #colnames(df_min)[max.col(df_min,ties.method="first")]
    df_min <- df_min %>% mutate(min = as.numeric(apply(df_min, 1, min)))
    
    df_max <- df %>% select(contains("Q3"))
    df_max <- type_convert(df_max)
    df_max <- df_max %>% mutate_all(funs(replace_na(., 1e6)))
    #colnames(df_max)[max.col(df_max,ties.method="first")]
    df_max <- df_max %>% mutate(max = as.numeric(apply(df_max, 1, min)))
    
    df_full <- cbind(df %>% select(cohort_id, cohort_type, tot_cases_n), df_med %>% select(med), df_min %>% select(min), df_max %>% select(max))
    df_full[df_full == 1e6] <- NA
    names(df_full)[names(df_full) == 'max'] <- paste0(param, "_max")
    names(df_full)[names(df_full) == 'min'] <- paste0(param, "_min")
    names(df_full)[names(df_full) == 'med'] <- paste0(param, "_med")
    df_full$data_descr <- "IQR"
    df_full$cohort_id <- paste0(df_full$cohort_id, " (n = ", as.character(df_full$tot_cases_n),")")
    write.csv(df_full, paste0("./data/cohort_", param, ".csv"))
    print(datatable(df_full, caption = paste0("overview of ", param)))
    return(df_full)
  }
}

#input = df_singlecases
#find = "max"
#param = "CRP"

collapse_labvals_single <- function(input, find, param){
  if (find == "max"){
    df <- input %>% select(contains(param) | contains("cohort_id"))
    print("Column extracted from single cases:")
    print(colnames(df))
    df_coll <- df %>% mutate_all(funs(replace_na(., -999)))
    df_coll <- type_convert(df_coll)
   # colnames(df_med)[max.col(df_med,ties.method="first")]
    df_coll <- df_coll %>% mutate(max = as.numeric(apply(df_coll, 1, max)))
    
    df_coll[df_coll == -999] <- NA
    names(df_coll)[names(df_coll) == 'max'] <- paste0(param, "_max")
    df_coll$data_descr <- "IQR"
    df_coll$cohort_id <- paste0("single cases (n = ", as.character(n_single_cases),")")
    write.csv(skim(df_coll), paste0("./data/singlecases_", param, ".csv"))
    return(df_coll)
  }
    else if (find == "min"){
    df <- input %>% select(contains(param) | contains("cohort_id"))
    print("Column extracted from single cases:")
    print(colnames(df))
    df_coll <- df %>% mutate_all(funs(replace_na(., 1e6)))
   # colnames(df_med)[max.col(df_med,ties.method="first")]
    df_coll <- df_coll %>% mutate(min = as.numeric(apply(df_coll, 1, min)))
    
    df_coll[df_coll == 1e6] <- NA
    names(df_coll)[names(df_coll) == 'min'] <- paste0(param, "_min")
    df_coll$cohort_id <- paste0("single cases (n = ", as.character(n_single_cases),")")
    write.csv(skim(df_coll), paste0("./data/singlecases_", param, ".csv"))
    return(df_coll)
  }
}


moveme <- function (df, movecommand) {
  invec <- names(df)
  
  movecommand <- lapply(strsplit(strsplit(movecommand, ";")[[1]], 
                                 ",|\\s+"), function(x) x[x != ""])
  movelist <- lapply(movecommand, function(x) {
    Where <- x[which(x %in% c("before", "after", "first", 
                              "last")):length(x)]
    ToMove <- setdiff(x, Where)
    list(ToMove, Where)
  })
  myVec <- invec
  for (i in seq_along(movelist)) {
    temp <- setdiff(myVec, movelist[[i]][[1]])
    A <- movelist[[i]][[2]][1]
    if (A %in% c("before", "after")) {
      ba <- movelist[[i]][[2]][2]
      if (A == "before") {
        after <- match(ba, temp) - 1
      }
      else if (A == "after") {
        after <- match(ba, temp)
      }
    }
    else if (A == "first") {
      after <- 0
    }
    else if (A == "last") {
      after <- length(myVec)
    }
    myVec <- append(temp, values = movelist[[i]][[1]], after = after)
  }
  
  df[,match(myVec, names(df))]
}

makeBarplot <- function(var_id_cohort, var_id_single, var_id){

        n_cohort <- df_cohort %>% select(tot_cases_n) %>% sum()#, outcome_death_n)
        var_cohort <- df_cohort[var_id_cohort] %>% sum(., na.rm = TRUE)#, outcome_death_n)
        
        n_single <- df_singlecases %>% nrow()
        
        var_single <- df_singlecases %>% filter(get(var_id_single) == TRUE) %>% nrow()
        
        n_all <- n_cohort + n_single
        var_all <- var_cohort + var_single
        
        bar_df_abs <- data.frame(x = c("cohort", "cohort", "single cases", "single cases", "all", "all"), col = c("total", var_id, "total", var_id, "total", var_id), vals = c(n_cohort, var_cohort, n_single, var_single, n_all, var_all) )
        
        bar_df_prct <- data.frame(x = c("cohort", "cohort", "single cases", "single cases", "all", "all"), col = c(paste0(var_id, " -"), paste0(var_id, " +"), paste0(var_id, " -"), paste0(var_id, " +"), paste0(var_id, " -"), paste0(var_id, " +")), vals = c(100-(var_cohort/n_cohort*100), var_cohort/n_cohort*100, 100-(var_single/n_single*100), var_single/n_single*100, 100-(var_all/n_all*100), var_all/n_all*100) )

        
        p_abs <- ggplot(bar_df_abs, aes(x = x, y =  vals, fill = col)) +
            geom_bar(stat = "identity", position = "dodge") +
            theme_bw() + 
            labs(title = paste0("Total cases vs ", var_id), subtitle = "Absolute numbers", x = "group", y = "n", col = "") +
  scale_fill_manual(values = wes_palette("Royal1"))
        
        
        p_prct <- ggplot(bar_df_prct, aes(x = x, y =  vals, fill = col)) +
            geom_bar(stat = "identity", position = "fill") +
            theme_bw() + 
            labs(title = paste0(var_id), subtitle = "Percent", x = "group", y = "%", col = "")  +
    scale_y_continuous(labels = scales::percent)+
  scale_fill_manual(values = wes_palette("Royal1"))
        
        ggarrange(p_abs, p_prct, legend = "bottom")
  
}

makeHeatmap_cohort <- function(param1, colname_single, exclude_single = NULL, plottitle, textsize = 3){
  var_cohort <- df_cohort %>% select(("cohort_id") | "tot_cases_n" | ( contains(param1) & contains("_n")))
  var_cohort$cohort_id <- paste0(var_cohort$cohort_id, " (n = ", as.character(var_cohort$tot_cases_n),")")
  var_cohort <- var_cohort %>% 
    gather(variable, value, 3:ncol(var_cohort)) %>% group_by(cohort_id, variable) %>% summarize(prct = value/tot_cases_n*100)
  var_cohort$variable <- sub("_n", "", var_cohort$variable)

if (!is.null(exclude_single)){
  var_single <- df_singlecases %>% select(-contains(exclude_single))
  var_single <- var_single %>% select(contains(colname_single))
} else
{
  var_single <- df_singlecases %>% select(contains(colname_single))
}

 #%>% select(-contains("any"))
cols <- sapply(var_single, is.logical)
var_single[,cols] <- lapply(var_single[,cols], as.numeric)
var_single <- colSums(var_single, na.rm = TRUE)
var_single <- var_single/nrow(df_singlecases)*100
var_single <- as.data.frame(var_single) %>% rownames_to_column()
var_single$cohort_id <- "single_cases"
colnames(var_single) <- c("variable", "prct", "cohort_id")


missing <- setdiff(var_single$variable, var_cohort$variable)
if (length(missing) != 0 ){
  missing_df <- data.frame(variable = missing, prct = NA, cohort_id = unique(var_cohort$cohort_id))
  var_cohort <- bind_rows(var_cohort, as_tibble(missing_df))
} else if (length(missing) == 0) {
  missing <- setdiff(var_cohort$variable, var_single$variable)
  if (length(missing) != 0){
  missing_df <- data.frame(variable = missing, prct = NA, cohort_id = unique(var_single$cohort_id))
  var_single <- bind_rows(var_single, as_tibble(missing_df))
  }
}

hm_cohort <- ggplot(var_cohort, aes(x = variable, y = cohort_id, fill = prct)) + 
    geom_tile() + theme_classic() +
    theme(axis.text.x=element_blank(), axis.ticks.x=element_blank(), axis.line=element_blank())+
   scale_fill_gradient(low = "yellow", high="red", na.value = "lightgray", limits = c(0,100)) +
    labs(x = "", y = "cohort", title =plottitle) +
    geom_text(aes(label=round(prct, 2)), size = textsize, color = "black")

hm_single <- ggplot(var_single, aes(x = variable, y = cohort_id, fill = prct)) + 
    geom_tile() +  theme_classic() +
    theme(axis.text.x=element_text(angle=90, hjust=1), axis.line=element_blank())+
    scale_fill_gradient(low = "yellow", high = "red", na.value = "lightgray", limits = c(0,100))+ labs(y = "cohort") +
    geom_text(aes(label=round(prct, 2)), size = textsize, color = "black") 

plot_grid(hm_cohort, hm_single, align = "v", nrow = 2, rel_heights = c(1/2, 1/2))
}
```


# Data import and cleaning
## Single cases
First, we import the single cases from the general excel sheet and transform the excel sheet so that variables are columns and rows are cases. Columns without any values are also removed. 

The single cases from Pouletty (10.1136/annrheumdis-2020-217960) are excluded (as they are included in the cohorts).


```{r}
df_singlecases <-
  read_excel("/Users/rmvpaeme/Onedrive/UGent/PIMS-TS Systematic Review - Data extractie/data extractie.xlsx",
             sheet = "Single cases",
             skip = 1,
             col_names = FALSE)[,-c(1:2)]
df_singlecases <- df_singlecases %>% t()
df_singlecases <- as.data.frame(df_singlecases, stringsAsFactors = FALSE)
nms <- as.vector(df_singlecases[1,])
nms[is.na(nms)] <- 'tmp'
colnames(df_singlecases) <- make.unique(as.character(nms))
df_singlecases <- df_singlecases[-1,]
df_singlecases <- df_singlecases %>% select(-contains("tmp"))
df_singlecases <- df_singlecases %>% select(-variable_id)

df_singlecases <- df_singlecases %>% 
  mutate_all(funs(str_replace(., "Yes", "yes")))
df_singlecases <- df_singlecases %>% 
  mutate_all(funs(str_replace(., "No", "no")))
df_singlecases <- df_singlecases %>% 
  mutate_all(funs(str_replace(., "pos", "yes")))
df_singlecases <- df_singlecases %>% 
  mutate_all(funs(str_replace(., "neg", "no")))

df_singlecases <- df_singlecases %>%
  replace_with_na_all(condition = ~.x == "NA")

df_singlecases <- type_convert(df_singlecases)
df_singlecases_inclPouletty <- df_singlecases
df_singlecases <- df_singlecases %>% filter(doi != "https://10.1136/annrheumdis-2020-217960")  # these cases are excluded according to the data sheet

df_singlecases <- df_singlecases[colSums(!is.na(df_singlecases)) > 0]
n_single_cases <- nrow(df_singlecases)
```

### Making summary statistics

In this section, data is summarized. For example, if there are any comorbidities present, a column "comorb_any" is added and annotated as TRUE. The same is done for COVID serology and symptoms of major organ (respiratory, cardiovascular etc).

```{r}
df_singlecases <- df_singlecases %>% mutate(comorb_any = apply(df_singlecases %>% select(contains("comorb")), 1, any))
df_singlecases <- df_singlecases %>% moveme(., "comorb_any before comorb_cardiovasc")
```

If IgG, IgA, IgM or COVID serology is reported as positive, the column covid_sero_any is annotated as TRUE.

```{r}
df_singlecases <- df_singlecases %>% mutate(covid_sero_any = apply(df_singlecases %>% select(covid_sero_pos, covid_IgA_pos, covid_IgM_pos, covid_IgG_pos), 1, any))

df_singlecases <- df_singlecases %>% moveme(., "covid_sero_any before covid_sero_pos")
```

If PCR+, stool PCR+, IgG, IgA, IgM or COVID serology is reported as positive, the column covid_pos_any is annotated as TRUE.

```{r}
df_singlecases <- df_singlecases %>% mutate(covid_pos_any = apply(df_singlecases %>% select(covid_PCR_pos, covid_PCR_stool_pos, covid_sero_pos, covid_IgA_pos, covid_IgM_pos, covid_IgG_pos), 1, any))

df_singlecases <- df_singlecases %>% moveme(., "covid_pos_any before covid_sero_any")
```

If any respiratory symptoms, symp_resp_any is annotated as TRUE.

```{r}
df_singlecases <- df_singlecases %>% mutate(symp_resp_any = apply(df_singlecases %>% select(symp_resp_NS, symp_resp_URT, symp_resp_dyspnea, symp_resp_pneumonia, symp_resp_failure, symp_resp_chestpain), 1, any))

df_singlecases <- df_singlecases %>% moveme(., "symp_resp_any before symp_resp_NS")
```

If any GI symptoms, symp_GI_any is annotated as TRUE.

```{r}
df_singlecases <- df_singlecases %>% mutate(symp_GI_any = apply(df_singlecases %>% select(contains("symp_GI")), 1, any))

df_singlecases <- df_singlecases %>% moveme(., "symp_GI_any before symp_GI_NS")
```

If any neurological symptoms, symp_neuro_any is annotated as TRUE.

```{r}
df_singlecases <- df_singlecases %>% mutate(symp_neuro_any = apply(df_singlecases %>% select(symp_neuro_headache,symp_neuro_meningitis,symp_neuro_meningism,symp_neuro_asthenia,symp_neuro_irritab), 1, any))

df_singlecases <- df_singlecases %>% moveme(., "symp_neuro_any before symp_neuro_GCS")
```

If any renal symptoms, symp_renal_any is annotated as TRUE.

```{r}
df_singlecases <- df_singlecases %>% mutate(symp_renal_any = apply(df_singlecases %>% select(symp_renal_AKI), 1, any))

df_singlecases <- df_singlecases %>% moveme(., "symp_renal_any before symp_renal_AKI")
```

If any cardiovascular symptoms, symp_cardiovasc_any is annotated as TRUE.

```{r}
df_singlecases <- df_singlecases %>% mutate(symp_cardiovasc_any = apply(df_singlecases %>% select(symp_cardiovasc_myocard,
symp_cardiovasc_pericard,
symp_cardiovasc_cordilat,
symp_cardiovasc_aneurysm,
symp_cardiovasc_shock,
symp_cardiovasc_tachycard,
symp_cardiovasc_arrhyt), 1, any))

df_singlecases <- df_singlecases %>% moveme(., "symp_cardiovasc_any before symp_cardiovasc_myocard")

write.csv(df_singlecases, paste0("./data/df_singlecases.csv"))

datatable(df_singlecases, caption = "Single cases dataframe")

```

## Cohorts
Afterwards, we do the same for the cohort sheet.

The papers by Grimaud et al. and Verdoni et al. are removed from the cohort dataframe, as most information is present in the single cases dataframe. 
```{r}
df_cohort <-
  read_excel("/Users/rmvpaeme/Onedrive/UGent/PIMS-TS Systematic Review - Data extractie/data extractie.xlsx",
             sheet = "Cohorts",
             skip = 1,
             col_names = FALSE)[,-c(1:3)]


df_cohort <- df_cohort %>% t()
df_cohort <- as.data.frame(df_cohort, stringsAsFactors = FALSE)
nms <- as.vector(df_cohort[1,])
nms[is.na(nms)] <- 'tmp'
colnames(df_cohort) <- make.unique(as.character(nms))
df_cohort <- df_cohort[-1,]
df_cohort <- df_cohort %>% select(-contains("tmp"))
df_cohort <- df_cohort %>% select(-variable_id)

df_cohort <- df_cohort %>% 
  mutate_all(funs(str_replace(., "Yes", "yes")))
df_cohort <- df_cohort %>% 
  mutate_all(funs(str_replace(., "No", "no")))
df_cohort <- df_cohort %>% 
  mutate_all(funs(str_replace(., "pos", "yes")))
df_cohort <- df_cohort %>% 
  mutate_all(funs(str_replace(., "neg", "no")))

df_cohort <- df_cohort %>%
  replace_with_na_all(condition = ~.x == "NA")

df_cohort <- type_convert(df_cohort)

df_cohort <- df_cohort[colSums(!is.na(df_cohort)) > 0]

df_cohort <- df_cohort %>% filter(doi != "https://doi.org/10.1186/s13613-020-00690-8") %>% filter(doi != "https://doi.org/10.1016/S0140-6736(20)31103-X")

df_cohort_controls <- df_cohort

df_cohort <- df_cohort %>% filter(cohort_type == "covid")

write.csv(df_cohort, paste0("./data/df_cohort.csv"))

datatable(df_cohort, caption = "Cohort dataframe")
```



# Descriptive statistics {.tabset}
## General


**Click on the any of the tabs above to see descriptive statistics for every variable**      



## Single cases
**How to read**  
Under "Variable type: logical", the number of true/falses are depicted. E.g. at the top we can see that there are 95 number of rows (= 95 patients). Overweight has 79 missing values (17% is complete), which means that 95-79=16 patients have either "TRUE" or "FALSE" for overweight. Of these 16, 9 are marked as "TRUE" for overweight. 

 <a href="#top">Download data as .csv on Github</a>

```{r}
skim(df_singlecases)
write.csv(skim(df_singlecases), paste0("./data/singlecases_descriptivestats.csv"))
```

## Cohorts
**How to read**  
The sum column equals the sum of all individuals, e.g. sum(tot_cases_n) means that there are 592 patients in total in the cohorts; sum(outcome_death_n) means that 9 patients died. 

The "Prct_total" column is the percentage of e.g. death (9/592). Only makes sense where n is reported e.g. therapy (not for lab values).

```{r}
skimsum <- skim_with(numeric = sfl(sum = ~ sum(., na.rm = TRUE), Prct_total = ~ sum(., na.rm = TRUE)/sum(df_cohort$tot_cases_n)*100), append = TRUE)
skimsum(df_cohort)
write.csv(skimsum(df_cohort), paste0("./data/cohort_descriptivestats.csv"))
```

## Historical controls
```{r}
df_cohort_controls_stats <- df_cohort_controls %>% filter(cohort_type == "control")
df_cohort_controls_stats <- df_cohort_controls_stats[colSums(!is.na(df_cohort_controls_stats)) > 0]
skimsum <- skim_with(numeric = sfl(sum = ~ sum(., na.rm = TRUE), Prct_total = ~ sum(., na.rm = TRUE)/sum(df_cohort_controls_stats$tot_cases_n)*100), append = TRUE)
skimsum(df_cohort_controls_stats)

write.csv(skimsum(df_cohort_controls_stats), paste0("./data/historicalcontrols_descriptivestats.csv"))

write.csv(df_cohort_controls_stats, paste0("./data/df_cohort_controls_stats.csv"))
```


# Data exploration
## Total cases and deaths
```{r}
#var_id_cohort = "outcome_death_n"
#var_id_single = "outcome_death"
#var_id = "deaths"
makeBarplot("outcome_death_n", "outcome_death", "deaths")
```

## Sex
```{r}
n_cohort <- df_cohort %>% select(tot_cases_n) %>% sum()
var_cohort <- df_cohort %>% select(contains("sex"))
var_cohort <- colSums(var_cohort, na.rm = TRUE)
var_cohort <- var_cohort/sum(df_cohort$tot_cases_n)*100
var_cohort["sex_na"] <- (100 - var_cohort["sex_m"] - var_cohort["sex_f"])

var_control <- df_cohort_controls %>% filter(cohort_id == "Pouletty - control") %>% select(contains("sex"))
var_control <- colSums(var_control, na.rm = TRUE)
var_control <- var_control/sum(df_cohort_controls %>% filter(cohort_id == "Pouletty - control") %>% select(tot_cases_n))*100
var_control["sex_na"] <- (100 - var_control["sex_m"] - var_control["sex_f"])

n_single <- df_singlecases %>% nrow()
var_single <- df_singlecases %>% select(contains("sex"))
var_single$sex_m <- ifelse(var_single$sex == "M", TRUE, FALSE)
var_single$sex_f <- ifelse(var_single$sex == "F", TRUE, FALSE)
cols <- sapply(var_single, is.logical)
var_single[,cols] <- lapply(var_single[,cols], as.numeric)
var_single <- colSums(var_single %>% select(-sex), na.rm = TRUE)
var_single <- var_single/nrow(df_singlecases)*100
var_single["sex_na"] <- (100 - var_single["sex_m"] - var_single["sex_f"])

bar_df_prct <- data.frame(
  x = c("males", "females", "missing", "males", "females", "missing", "males", "females", "missing"),
  vals = c(var_single, var_cohort, var_control),
  col = c(rep("single", length(var_single)), rep("cohorts", length(var_cohort)), rep("histor ctrl", length(var_control))
))

p_prct <- ggplot(bar_df_prct, aes(x = col, y =  vals, fill = x)) +
    geom_bar(stat = "identity", position = "stack") +
    theme_bw() + 
    labs(title = "Male/female distribution in dataset", subtitle = "Prct", x = "sex", y = "%", col = " ")  + lims(y = c(0,100)) + theme(axis.text.x=element_text(angle=90, hjust=1))+
  scale_fill_manual(values = wes_palette("Royal1"))
p_prct
```


```{r}
var_cohort <- df_cohort %>% select(contains("sex") | ("cohort_id") | "tot_cases_n")
sex_f <- var_cohort %>% group_by(cohort_id) %>% summarize(prct = sex_f/tot_cases_n) %>%  mutate(sex = "female")
sex_m <- var_cohort %>% group_by(cohort_id) %>% summarize(prct = sex_m/tot_cases_n) %>% mutate(sex = "male")
sex_all <- rbind(sex_f, sex_m)

p_sex_cohort <- ggplot(sex_all, aes(y = cohort_id, x = prct, fill = sex)) + 
          geom_bar(stat = "identity", position = "fill") + 
          theme_bw() + labs(x = "") + 
          scale_fill_manual(values = wes_palette("Royal1"))

var_controls <- df_cohort_controls %>% filter(cohort_id == "Pouletty - control") %>% select(contains("sex") | ("cohort_id") | "tot_cases_n")
sex_f <- var_controls %>% group_by(cohort_id) %>% summarize(prct = sex_f/tot_cases_n) %>% mutate(sex = "female")
sex_m <- var_controls %>% group_by(cohort_id) %>% summarize(prct = sex_m/tot_cases_n) %>% mutate(sex = "male")
sex_all <- rbind(sex_f, sex_m)

p_sex_controls <- ggplot(sex_all, aes(y = cohort_id, x = prct, fill = sex)) + 
          geom_bar(stat = "identity", position = "fill") + 
          theme_bw() + labs(x = "") + 
          scale_fill_manual(values = wes_palette("Royal1"))

n_single <- df_singlecases %>% nrow()
var_single <- df_singlecases %>% select(contains("sex"))
var_single$sex_m <- ifelse(var_single$sex == "M", TRUE, FALSE)
var_single$sex_f <- ifelse(var_single$sex == "F", TRUE, FALSE)
cols <- sapply(var_single, is.logical)
var_single[,cols] <- lapply(var_single[,cols], as.numeric)
var_single <- colSums(var_single %>% select(-sex), na.rm = TRUE)
var_single <- var_single/nrow(df_singlecases)*100

sex_single <- data.frame(cohort_id = "single_cases", prct = c(var_single["sex_m"], var_single["sex_f"]), sex = c("male", "female"))

p_sex_single <- ggplot(sex_single, aes(y = cohort_id, x = prct, fill = sex)) + 
          geom_bar(stat = "identity", position = "fill") + 
          theme_bw() + 
          scale_fill_manual(values = wes_palette("Royal1"))

a <- plot_grid(p_sex_cohort, p_sex_controls, p_sex_single, align = "v", nrow = 3, rel_heights = c(5/7, 1/7, 1/7))
a
```

## Age distribution

```{r}
cohort_age <- df_cohort_controls %>% select(contains("cohort_id") | contains("age") | contains("cohort_type")  | contains("tot_cases_n"))
cohort_age$cohort_id <- paste0(cohort_age$cohort_id, " (n = ", cohort_age$tot_cases_n,")")
cohort_age$age_med_yrs <- as.numeric(cohort_age$age_med_yrs )
cohort_age$age_Q1_yrs <- as.numeric(cohort_age$age_Q1_yrs)
cohort_age$age_Q3_yrs <- as.numeric(cohort_age$age_Q3_yrs)
cohort_age$age_min_yrs <- as.numeric(cohort_age$age_min_yrs)
cohort_age$age_max_yrs <- as.numeric(cohort_age$age_max_yrs)

cohort_age$data_descr <- ifelse(!is.na(cohort_age$age_Q1_yrs) & is.na(cohort_age$age_min_yrs) , "IQR", 
                                ifelse(is.na(cohort_age$age_Q1_yrs) & !is.na(cohort_age$age_min_yrs), "range", 
                                    ifelse(!is.na(cohort_age$age_Q1_yrs) & !is.na(cohort_age$age_min_yrs), "both", "none")))

p_age_cohort <- ggplot(cohort_age %>% filter(cohort_type == "covid"), aes(y = cohort_id, x = age_med_yrs, col = data_descr)) + 
        geom_point(size = 4) + 
        geom_errorbar(aes(xmin=age_Q1_yrs, xmax=age_Q3_yrs), width=.8, position=position_dodge(.9)) +
        geom_errorbar(aes(xmin=age_min_yrs,  xmax=age_max_yrs), width=.2, position=position_dodge(.9)) +
        theme_bw() + lims(x = c(0,21)) + 
        labs(y = "cohort", x = "", col = "bars") + theme(legend.position="top")+
        scale_color_manual(values = c(wes_palette("BottleRocket2")[1:3], wes_palette("BottleRocket1")[2]))

p_age_controls <- ggplot(cohort_age %>% filter(cohort_type != "covid"), aes(y = cohort_id, x = age_med_yrs, col = data_descr)) + 
        geom_point(size = 4) + 
        geom_errorbar(aes(xmin=age_Q1_yrs, xmax=age_Q3_yrs), width=.2, position=position_dodge(.9)) +
        geom_errorbar(aes(xmin=age_min_yrs,  xmax=age_max_yrs), width=.2, position=position_dodge(.9)) +
        theme_bw() + lims(x = c(0,21)) +
        labs(y = "cohort", x = "", col = "bars") + theme(legend.position="none")+
        scale_color_manual(values = wes_palette("BottleRocket2")[2])

p_age_single <- ggplot(df_singlecases, aes(x = as.numeric(age), y = paste0("single cases (n = ", n_single,")"))) +
      geom_violin(fill = wes_palette("Darjeeling2")[4]) + 
      geom_boxplot(width=.3, fill = wes_palette("Darjeeling2")[1]) + 
      theme_bw() + geom_beeswarm(groupOnX=FALSE, alpha = 0.5) + lims(x = c(0,21)) + 
      labs(y = "cohort", x = "Age (years)")

a <- plot_grid(p_age_cohort, p_age_controls, p_age_single, align = "v", nrow = 3, rel_heights = c(2/3, 1/5, 1/3))
a
```


## Symptoms 
### Single cases {.tabset}
#### All symptoms
```{r}
makeUpsetR <- function(input_df){
var_single <- input_df 
cols <- sapply(var_single, is.logical)
var_single[,cols] <- lapply(var_single[,cols], as.numeric)

var_single_upsetr <- var_single 
var_single_upsetr[is.na(var_single_upsetr)] <- 0
var_single_upsetr <- as.data.frame(var_single_upsetr)
for(i in 1:ncol(var_single_upsetr)){ var_single_upsetr[ , i] <- as.integer(var_single_upsetr[ , i]) }
upset(var_single_upsetr, sets = c(colnames(var_single_upsetr)), sets.bar.color = "#56B4E9",
order.by = "freq", keep.order = TRUE)#, empty.intersections = "on", keep.order = FALSE)
}

makeUpsetR(df_singlecases %>% select(contains( "symp")) %>% select(contains("any")))
```

#### Respiratory
```{r}
makeUpsetR(df_singlecases %>% select(contains("symp")) %>% select(contains("resp")) %>% select(-contains("any")))
```

#### Cardiovascular
```{r}
makeUpsetR(df_singlecases %>% select(contains("symp")) %>% select(contains("cardiovasc")) %>% select(-contains("LVEF")) %>% select(-contains("any")))
```

#### GI
```{r}
makeUpsetR(df_singlecases %>% select(contains("symp")) %>% select(contains("GI")) %>% select(-contains("neuro")) %>% select(-contains("any")))
```

### Single cases + cohort {.tabset}
#### Respiratory
```{r}
barSymp <- function(colname_chort, colname_single, exclude_single = NULL, plottitle){

var_cohort <- df_cohort %>% 
                        select(contains("cohort_id") | contains("tot_cases_n") | (contains(colname_chort) & contains("_n")))

var_cohort <- var_cohort %>% 
        gather(variable, value, 3:ncol(var_cohort)) %>% 
        drop_na(value)  %>% group_by(variable) %>% 
        summarize(prct = sum(value)/sum(tot_cases_n)*100)

var_cohort <- setNames(var_cohort$prct, var_cohort$variable)
names(var_cohort) <- sub("_n", "", names(var_cohort))

n_single <- df_singlecases %>% nrow()

if (!is.null(exclude_single)){
  var_single <- df_singlecases %>% select(-contains(exclude_single))
  var_single <- var_single %>% select(contains(colname_single))
} else
{
  var_single <- df_singlecases %>% select(contains(colname_single))
}

 #%>% select(-contains("any"))
cols <- sapply(var_single, is.logical)
var_single[,cols] <- lapply(var_single[,cols], as.numeric)
var_single <- colSums(var_single, na.rm = TRUE)
var_single <- var_single/nrow(df_singlecases)*100

bar_df_prct <- data.frame(
  x = c(names(var_single), names(var_cohort)),
  vals = c(var_single, var_cohort),
  col = c(rep("single", length(var_single)), rep("cohorts", length(var_cohort)))
)

p_prct <- ggplot(bar_df_prct, aes(x = x, y =  vals, fill = col)) +
    geom_bar(stat = "identity", position = "dodge") +
    theme_bw() + 
    labs(title = plottitle, 
          subtitle = "Percent of group", x = "treatment", y = "%", col = " ")  + 
          theme(axis.text.x=element_text(angle=90, hjust=1))+
          scale_fill_manual(values = wes_palette("Royal1"))
p_prct
}

makeHeatmap_cohort("symp_resp", "symp_resp", plottitle = "Cases with respiratory symptoms, per cohort")

barSymp("symp_resp", "symp_resp", plottitle = "Cases with respiratory symptoms")
```


```{r}
# var_cohort <- df_cohort %>% select(("cohort_id") | "tot_cases_n" |( contains("symp_resp") & contains("n")))
# 
# resp_symp_cohort <- var_cohort %>% 
#   gather(variable, value, 3:ncol(var_cohort)) %>% group_by(cohort_id, variable) %>% summarize(prct = value/tot_cases_n)
# 
# ggplot(resp_symp_cohort, aes(x = prct, y = cohort_id, col = variable)) + geom_point()
```

#### Cardiovascular
```{r}
makeHeatmap_cohort("symp_cardiovasc", "symp_cardiovasc", exclude_single = "symp_cardiovasc_LVEF", plottitle = "Cases with cardiovascular symptoms, per cohort")

barSymp("symp_cardiovasc", "symp_cardiovasc", exclude_single = "symp_cardiovasc_LVEF", plottitle = "Cases with cardiovascular symptoms")
```

#### Gastro-intestinal
```{r}
makeHeatmap_cohort("symp_GI", "symp_GI", plottitle = "Cases with GI symptoms, per cohort")

barSymp("symp_GI", "symp_GI", plottitle = "Cases with GI symptoms")
```

## COVID contact
```{r}
var_cohort <- df_cohort %>% select(("cohort_id" | "tot_cases_n") | ( contains("covid") & contains("_n") & (contains("pos") | contains("closecont")  | contains("any"))))
var_cohort$cohort_id <- paste0(var_cohort$cohort_id, " (n = ", as.character(var_cohort$tot_cases_n),")")

var_cohort <- var_cohort %>% 
  gather(variable, value, 3:ncol(var_cohort)) %>% group_by(cohort_id, variable) %>% summarize(prct = value/tot_cases_n*100)

var_cohort$variable <- sub("n_", "", var_cohort$variable)

var_single <- df_singlecases %>% select(contains("covid"))
cols <- sapply(var_single, is.logical)
var_single[,cols] <- lapply(var_single[,cols], as.numeric)
var_single <- colSums(var_single, na.rm = TRUE)
var_single <- var_single/nrow(df_singlecases)*100
var_single <- as.data.frame(var_single) %>% rownames_to_column()
var_single$cohort_id <- "single_cases"
colnames(var_single) <- c("variable", "prct", "cohort_id")


missing <- setdiff(var_single$variable, var_cohort$variable)
if (length(missing) != 0 ){
  missing_df <- data.frame(variable = missing, prct = rep(NA, length(missing)), cohort_id = rep(unique(var_cohort$cohort_id), length(missing)))
  var_cohort <- bind_rows(var_cohort, as_tibble(missing_df))
} 

missing <- setdiff(var_cohort$variable, var_single$variable)

if (length(missing) != 0) {
  if (length(missing) != 0){
data.frame(variable = missing, prct = rep(NA, length(missing)), cohort_id = rep(unique(var_single$cohort_id), length(missing)))
  var_single <- bind_rows(var_single, as_tibble(missing_df))
  }
}


hm_cohort <- ggplot(var_cohort, aes(x = variable, y = cohort_id, fill = prct)) + 
    geom_tile() + theme_classic() +
    theme(axis.text.x=element_blank(), axis.ticks.x=element_blank(), axis.line=element_blank())+
   scale_fill_gradient(low = "yellow", high="red", na.value = "lightgray", limits = c(0,100)) +
    labs(x = "", y = "cohort", title = "COVID symptoms, per cohort") +
    geom_text(aes(label=round(prct, 2)), size = 3, color = "black")

hm_single <- ggplot(var_single, aes(x = variable, y = cohort_id, fill = prct)) + 
    geom_tile() +  theme_classic() +
    theme(axis.text.x=element_text(angle=90, hjust=1), axis.line=element_blank())+
    scale_fill_gradient(low = "yellow", high = "red", na.value = "lightgray", limits = c(0,100))+ labs(y = "cohort") +
    geom_text(aes(label=round(prct, 2)), size = 3, color = "black") 

plot_grid(hm_cohort, hm_single, align = "v", nrow = 2, rel_heights = c(1/2, 1/2))

```

```{r}
var_cohort <- df_cohort %>% 
                        select(contains("cohort_id") | contains("tot_cases_n") | contains("covid") & contains("_n") & (contains("_pos") | contains("close")))

covid_cohort <- var_cohort %>% 
        gather(variable, value, 3:ncol(var_cohort)) %>% 
        drop_na(value)  %>% group_by(variable) %>% 
        summarize(prct = sum(value)/sum(tot_cases_n)*100)

covid_cohort <- setNames(covid_cohort$prct, covid_cohort$variable)

n_single <- df_singlecases %>% nrow()
var_single <- df_singlecases %>% select(contains("covid")) 
cols <- sapply(var_single, is.logical)
var_single[,cols] <- lapply(var_single[,cols], as.numeric)

makeUpsetR(df_singlecases %>% select(contains("covid")) %>% select(-contains("covid_IgM_pos")) %>% select(-contains("covid_IgA_pos"))  %>% select(-contains("covid_IgG_pos"))  %>% select(-contains("covid_sero_pos")) )

var_single <- colSums(var_single, na.rm = TRUE)
var_single <- var_single/nrow(df_singlecases)*100

bar_df_prct <- data.frame(
  x = c("close contact reported", "PCR +", "stool +","PCR or stool or sero +", "any serology +", "sero + further NS", "IgA +", "IgM +", "IgG +", "close contact reported", "IgA +", "IgG +", "IgM +", "PCR +", "sero + further NS", "stool +"),
  vals = c(var_single, covid_cohort),
  col = c(rep("single", length(var_single)), rep("cohorts", length(covid_cohort)))
)

p_prct <- ggplot(bar_df_prct, aes(x = x, y =  vals, fill = col)) +
    geom_bar(stat = "identity", position = "dodge") +
    theme_bw() + 
    labs(title = "SARS-CoV2 testing", 
         subtitle = "Prct", x = "variable", y = "%", col = " ") +
    theme(axis.text.x=element_text(angle=90, hjust=1)) +
    scale_fill_manual(values = wes_palette("Royal1"))
#p_prct

neither_PCR_Ig <- nrow(df_singlecases %>% filter((covid_sero_any == FALSE | is.na(covid_sero_any)) & (covid_PCR_pos == FALSE | is.na(covid_PCR_pos)) & (covid_PCR_stool_pos == FALSE | is.na(covid_PCR_stool_pos))))

neither_PCR_Ig_closecontact <-
  nrow(df_singlecases %>% filter((covid_sero_any == FALSE |
                                    is.na(covid_sero_any)) &
                                   (covid_PCR_pos == FALSE |
                                      is.na(covid_PCR_pos)) &
                                   (covid_PCR_stool_pos == FALSE |
                                      is.na(covid_PCR_stool_pos)) &
                                   (covid_closecontact == FALSE | is.na(covid_closecontact))
  ))

print(paste0("Cases with neither PCR nor serology: ", neither_PCR_Ig))

print(paste0("Cases with neither PCR nor serology nor closecontact: ", neither_PCR_Ig_closecontact))
```

## Kawasaki criteria

```{r}
makeHeatmap_cohort("kawasaki", "kawasaki",exclude_single = "koyobas", plottitle = "Cases with kawasaki symptoms, per cohort")

barSymp("kawasaki", "kawasaki", exclude_single = "koyobas", plottitle = "Kawasaki symptoms")
```

## Shock
```{r}
makeBarplot("symp_cardiovasc_shock_n", "symp_cardiovasc_shock", "Shock")
```

## Lab values {.tabset}
For lab values, sometimes multiple values are reported (baseline, peak or not-specified). All lab values are collapsed based on the max (or the min for e.g. hemoglobin): so only the highest value of median, Q1 or Q3 is used. 

### C-reactive protein

```{r}
crp_collapse_cohort <- collapse_labvals_cohort(df_cohort_controls, "max", "CRP")
crp_collapse_single <- collapse_labvals_single(df_singlecases, "max", "CRP")
crp_missing <- sum(is.na(crp_collapse_single$CRP_max))

p_crp_cohort <- ggplot(crp_collapse_cohort, aes(y = cohort_id, x = CRP_med, col = cohort_type)) + 
        geom_point() +  
        geom_errorbar(aes(xmin=CRP_min, xmax=CRP_max), width=.2, position=position_dodge(.9)) + lims(x = c(0,600)) + 
        theme_bw() + labs(title = "CRP", y = "cohort", x = "") +
        geom_vline(xintercept = co_CRP, linetype = "dashed", color = "black") + theme(legend.justification = c(1, 1), legend.position = c(0.98, 0.98), legend.title=element_blank()) +
        scale_color_manual(values = wes_palette("Royal1"))

p_crp_single <- ggplot(crp_collapse_single, aes(x = as.numeric(CRP_max), y = cohort_id)) +
      geom_violin(fill = wes_palette("Darjeeling2")[4]) + 
      geom_boxplot(width=.3, fill =  wes_palette("Darjeeling2")[1]) + 
      theme_bw() + geom_beeswarm(groupOnX=FALSE, alpha = 0.5) + lims(x = c(0,600)) + labs(y = "", x = "CRP (mg/dL)", subtitle = paste0("missing data for ", crp_missing, " cases")) +
      geom_hline(yintercept = co_CRP, linetype = "dashed", color = "black")

CRP_grid <- plot_grid(p_crp_cohort, p_crp_single, align = "v", nrow = 2, rel_heights = c(2/3, 1/3))
CRP_grid
```

### Lymphocytes
```{r}
lympho_collapse_cohort <- collapse_labvals_cohort(df_cohort_controls, "min", "lympho")
lympho_collapse_single <- collapse_labvals_single(df_singlecases, "min", "lympho")
lympho_missing <- sum(is.na(lympho_collapse_single$lympho_min))

p_lympho_cohort <- ggplot(lympho_collapse_cohort, aes(y = cohort_id, x = lympho_med, col = cohort_type)) + 
        geom_point() +  
        geom_errorbar(aes(xmin=lympho_min, xmax=lympho_max), width=.2, position=position_dodge(.9)) + 
        theme_bw() + labs(title = "lymphocytes", y = "", x = "") + lims(x = c(0,7500))  +
         geom_vline(xintercept = co_lympho, linetype = "dashed", color = "black") + theme(legend.justification = c(1, 1), legend.position = c(0.98, 0.98), legend.title=element_blank()) +
        scale_color_manual(values = wes_palette("Royal1"))#+
        #rremove("y.text") 

p_lympho_single <- ggplot(lympho_collapse_single, aes(x = as.numeric(lympho_min), y = cohort_id)) +
      geom_violin(fill = wes_palette("Darjeeling2")[4]) + 
      geom_boxplot(width=.3, fill = wes_palette("Darjeeling2")[1]) + 
      lims(x = c(0,7500))+
      theme_bw() + geom_beeswarm(groupOnX=FALSE, alpha = 0.5)  + labs(y = "", x = "Lymphocytes (/µL)", subtitle = paste0("missing data for ", lympho_missing, " cases")) +
      geom_vline(xintercept = co_lympho, linetype = "dashed", color = "black") #+ 
      #rremove("y.text") 

lympho_grid <- plot_grid(p_lympho_cohort, p_lympho_single, align = "v", nrow = 2, rel_heights = c(2/3, 1/3))
lympho_grid
```

### White blood cells
```{r}
wbc_collapse_cohort <- collapse_labvals_cohort(df_cohort_controls, "max", "WBC")
wbc_collapse_single <- collapse_labvals_single(df_singlecases, "max", "WBC")
wbc_missing <- sum(is.na(wbc_collapse_single$WBC_max))

p_wbc_cohort <- ggplot(wbc_collapse_cohort, aes(y = cohort_id, x = WBC_med, col = cohort_type)) + 
        geom_point() +  
        geom_errorbar(aes(xmin=WBC_min, xmax=WBC_max), width=.2, position=position_dodge(.9)) + lims(x = c(0,50000)) + 
        theme_bw() + labs(title = "WBC", y = "cohort", x = "")  +
        geom_vline(xintercept = co_WBC, linetype = "dashed", color = "black")  + theme(legend.justification = c(1, 1), legend.position = c(0.98, 0.98), legend.title=element_blank()) +
        scale_color_manual(values = wes_palette("Royal1"))

p_wbc_single <- ggplot(wbc_collapse_single, aes(x = as.numeric(WBC_max), y = cohort_id)) +
      geom_violin(fill = wes_palette("Darjeeling2")[4]) + 
      geom_boxplot(width=.3, fill = wes_palette("Darjeeling2")[1]) + 
      theme_bw() + geom_beeswarm(groupOnX=FALSE, alpha = 0.5) + labs(y = "", x = "WBC (/µL)", subtitle = paste0("missing data for ", wbc_missing, " cases")) + lims(x = c(0,50000)) +
      geom_vline(xintercept = co_WBC, linetype = "dashed", color = "black") 

WBC_grid <- plot_grid(p_wbc_cohort, p_wbc_single, align = "v", nrow = 2, rel_heights = c(2/3, 1/3))
WBC_grid
```


### Ferritin
```{r}
ferritin_collapse_cohort <- collapse_labvals_cohort(df_cohort_controls, "max", "ferrit")
ferritin_collapse_single <- collapse_labvals_single(df_singlecases, "max", "ferrit")
ferritin_missing <- sum(is.na(ferritin_collapse_single$ferrit_max))

p_ferritin_cohort <- ggplot(ferritin_collapse_cohort, aes(y = cohort_id, x = ferrit_med, col = cohort_type)) + 
        geom_point() +  
        geom_errorbar(aes(xmin=ferrit_min, xmax=ferrit_max), width=.2, position=position_dodge(.9)) + lims(x = c(0,11000)) + 
        theme_bw() + labs(title = "Ferritin", y = "cohort", x = "") +
        geom_vline(xintercept = co_ferritin, linetype = "dashed", color = "black") + theme(legend.justification = c(1, 1), legend.position = c(0.98, 0.98), legend.title=element_blank()) +
        scale_color_manual(values = wes_palette("Royal1"))

p_ferritin_single <- ggplot(ferritin_collapse_single, aes(x = as.numeric(ferrit_max), y = cohort_id)) +
      geom_violin(fill = wes_palette("Darjeeling2")[4]) + 
      geom_boxplot(width=.3, fill = wes_palette("Darjeeling2")[1]) + 
      theme_bw() + geom_beeswarm(groupOnX=FALSE, alpha = 0.5) + labs(y = "", x = "Ferritin (µg/l)", subtitle = paste0("missing data for ", ferritin_missing, " cases")) + lims(x = c(0,11000)) +
      geom_vline(xintercept = co_ferritin, linetype = "dashed", color = "black") 

ferritin_grid <- plot_grid(p_ferritin_cohort, p_ferritin_single, align = "v", nrow = 2, rel_heights = c(2/3, 1/3))
ferritin_grid
```


### Troponin
```{r}
troponin_collapse_cohort <- collapse_labvals_cohort(df_cohort_controls, "max", "troponin")
troponin_collapse_single <- collapse_labvals_single(df_singlecases, "max", "troponin")
troponin_missing <- sum(is.na(troponin_collapse_single$troponin_max))

p_troponin_cohort <- ggplot(troponin_collapse_cohort, aes(y = cohort_id, x = troponin_med, col = cohort_type)) + 
        geom_point() +  
        geom_errorbar(aes(xmin=troponin_min, xmax=troponin_max), width=.2, position=position_dodge(.9)) + lims(x = c(0,7000)) + 
        theme_bw() + labs(title = "Troponin", y = "cohort", x = "")  +
        geom_vline(xintercept = co_tropo, linetype = "dashed", color = "black")  + theme(legend.justification = c(1, 1), legend.position = c(0.98, 0.98), legend.title=element_blank()) +
        scale_color_manual(values = wes_palette("Royal1"))

p_troponin_single <- ggplot(troponin_collapse_single, aes(x = as.numeric(troponin_max), y = cohort_id)) +
      geom_violin(fill = wes_palette("Darjeeling2")[4]) + 
      geom_boxplot(width=.3, fill = wes_palette("Darjeeling2")[1]) + 
      theme_bw() + geom_beeswarm(groupOnX=FALSE, alpha = 0.5) + labs(y = "", x = "Troponin (ng/L)", subtitle = paste0("missing data for ", troponin_missing, " cases")) + lims(x = c(0,7000)) +
      geom_vline(xintercept = co_tropo, linetype = "dashed", color = "black") 

troponin_grid <- plot_grid(p_troponin_cohort, p_troponin_single, align = "v", nrow = 2, rel_heights = c(2/3, 1/3))
troponin_grid
```


### IL-6
Note: The cases from Pouletty et al are added to the single cases as they report on IL6 values. 

```{r}
IL6_collapse_cohort <- collapse_labvals_cohort(df_cohort_controls, "max", "IL6")
IL6_collapse_single <- collapse_labvals_single(df_singlecases_inclPouletty, "max", "IL6")
IL6_missing <- sum(is.na(IL6_collapse_single$IL6_max))

p_IL6_cohort <- ggplot(IL6_collapse_cohort, aes(y = cohort_id, x = IL6_med)) + 
        geom_point() +  
        geom_errorbar(aes(xmin=IL6_min, xmax=IL6_max), width=.2, position=position_dodge(.9)) + lims(x = c(0,2500)) + 
        theme_bw() + labs(title = "IL6", y = "cohort", x = "")  +
        geom_vline(xintercept = co_IL6, linetype = "dashed", color = "black")  +
        scale_color_manual(values = wes_palette("Royal1"))

p_IL6_single <- ggplot(IL6_collapse_single, aes(x = as.numeric(IL6_max), y = cohort_id)) +
      geom_violin(fill = wes_palette("Darjeeling2")[4]) + 
      geom_boxplot(width=.3, fill = wes_palette("Darjeeling2")[1]) + 
      theme_bw() + geom_beeswarm(groupOnX=FALSE, alpha = 0.5) + labs(y = "", x = "IL6 (pg/ml)", subtitle = paste0("missing data for ", IL6_missing, " cases")) + lims(x = c(0,2500)) +
      geom_vline(xintercept = co_IL6, linetype = "dashed", color = "black") 

IL6_grid <- plot_grid(p_IL6_cohort, p_IL6_single, align = "v", nrow = 2, rel_heights = c(2/3, 1/3))
IL6_grid
```


### BNP

```{r}
collapse_cohort <- collapse_labvals_cohort(df_cohort_controls, "max", "_BNP")
collapse_single <- collapse_labvals_single(df_singlecases, "max", "_BNP")
missing <- sum(is.na(collapse_single$`_BNP_max`))

p_BNP_cohort <- ggplot(collapse_cohort, aes(y = cohort_id, x = `_BNP_med`, col = cohort_type)) + 
        geom_point() +  
        geom_errorbar(aes(xmin=`_BNP_min`, xmax=`_BNP_max`), width=.2, position=position_dodge(.9)) + lims(x = c(0,20000)) + 
        theme_bw() + labs(title = "BNP", y = "cohort", x = "")  +
        geom_vline(xintercept = co_BNP, linetype = "dashed", color = "black")  +theme(legend.justification = c(1, 1), legend.position = c(0.98, 0.98)) +
        scale_color_manual(values = wes_palette("Royal1"))

p_BNP_single <- ggplot(collapse_single, aes(x = as.numeric(`_BNP_max`), y = cohort_id)) +
      geom_violin(fill = wes_palette("Darjeeling2")[4]) + 
      geom_boxplot(width=.3, fill = wes_palette("Darjeeling2")[1]) + 
      theme_bw() + geom_beeswarm(groupOnX=FALSE, alpha = 0.5) + labs(y = "", x = "BNP (pg/ml)", subtitle = paste0("missing data for ", missing, " cases")) + lims(x = c(0,20000)) +
      geom_vline(xintercept = co_BNP, linetype = "dashed", color = "black") 

BNP_grid <- plot_grid(p_BNP_cohort, p_BNP_single, align = "v", nrow = 2, rel_heights = c(2/3, 1/3))
BNP_grid
```

### NTproBNP
```{r}
collapse_cohort <- collapse_labvals_cohort(df_cohort_controls, "max", "NTproBNP")
collapse_single <- collapse_labvals_single(df_singlecases, "max", "NTproBNP")
missing <- sum(is.na(collapse_single$NTproBNP_max))

p_NTproBNP_cohort <- ggplot(collapse_cohort, aes(y = cohort_id, x = NTproBNP_med, col = cohort_type)) + 
        geom_point() +  
        geom_errorbar(aes(xmin=NTproBNP_min, xmax=NTproBNP_max), width=.2, position=position_dodge(.9)) + lims(x = c(0,70000)) + 
        theme_bw() + labs(title = "NTproBNP", y = "cohort", x = "")  +
        geom_vline(xintercept = co_NTproBNP, linetype = "dashed", color = "black") + theme(legend.justification = c(1, 1), legend.position = c(0.98, 0.98), legend.title=element_blank()) +
        scale_color_manual(values = wes_palette("Royal1"))

p_NTproBNP_single <- ggplot(collapse_single, aes(x = as.numeric(NTproBNP_max), y = cohort_id)) +
      geom_violin(fill = wes_palette("Darjeeling2")[4]) + 
      geom_boxplot(width=.3, fill = wes_palette("Darjeeling2")[1]) + 
      theme_bw() + geom_beeswarm(groupOnX=FALSE, alpha = 0.5) + labs(y = "", x = "NTproBNP (pg/ml)", subtitle = paste0("missing data for ", missing, " cases")) + lims(x = c(0,70000)) +
      geom_vline(xintercept = co_NTproBNP, linetype = "dashed", color = "black") 

NTproBNP_grid <- plot_grid(p_NTproBNP_cohort, p_NTproBNP_single, align = "v", nrow = 2, rel_heights = c(2/3, 1/3))
NTproBNP_grid
```


### Platelets

```{r}
collapse_cohort <- collapse_labvals_cohort(df_cohort_controls, "min", "platelet")
collapse_single <- collapse_labvals_single(df_singlecases, "min", "platelet")
missing <- sum(is.na(collapse_single$platelet_min))

p_platelet_cohort <- ggplot(collapse_cohort, aes(y = cohort_id, x = platelet_med, col = cohort_type)) + 
        geom_point() +  
        geom_errorbar(aes(xmin=platelet_min, xmax=platelet_max, col=cohort_type), width=.2, position=position_dodge(.9)) + lims(x = c(0,750000)) + 
        theme_bw() + labs(title = "platelet", y = "cohort", x = "")  +
        geom_vline(xintercept = co_platelet, linetype = "dashed", color = "black")  + theme(legend.justification = c(1, 1), legend.position = c(0.98, 0.98), legend.title=element_blank()) +
        scale_color_manual(values = wes_palette("Royal1"))

p_platelet_single <- ggplot(collapse_single, aes(x = as.numeric(platelet_min), y = cohort_id)) +
      geom_violin(fill = wes_palette("Darjeeling2")[4]) + 
      geom_boxplot(width=.3, fill = wes_palette("Darjeeling2")[1]) + 
      theme_bw() + geom_beeswarm(groupOnX=FALSE, alpha = 0.5) + labs(y = "", x = "Platelets (/µL)", subtitle = paste0("missing data for ", missing, " cases")) + lims(x = c(0,750000)) +
      geom_vline(xintercept = co_platelet, linetype = "dashed", color = "black") 

platelet_grid <- plot_grid(p_platelet_cohort, p_platelet_single, align = "v", nrow = 2, rel_heights = c(2/3, 1/3))
platelet_grid
```


### D-dimers

```{r}
collapse_cohort <- collapse_labvals_cohort(df_cohort_controls, "max", "Ddim")
collapse_single <- collapse_labvals_single(df_singlecases, "max", "Ddim")
missing <- sum(is.na(collapse_single$Ddim_max))

p_Ddim_cohort <- ggplot(collapse_cohort, aes(y = cohort_id, x = Ddim_med, col = cohort_type)) + 
        geom_point() +  
        geom_errorbar(aes(xmin=Ddim_min, xmax=Ddim_max, col=cohort_type), width=.2, position=position_dodge(.9)) + lims(x = c(0,11000)) + 
        theme_bw() + labs(title = "D-dimers", y = "cohort", x = "")  +
        geom_vline(xintercept = co_Ddim, linetype = "dashed", color = "black")  + theme(legend.justification = c(1, 1), legend.position = c(0.98, 0.98), legend.title=element_blank()) +
        scale_color_manual(values = wes_palette("Royal1"))

p_Ddim_single <- ggplot(collapse_single, aes(x = as.numeric(Ddim_max), y = cohort_id)) +
      geom_violin(fill = wes_palette("Darjeeling2")[4]) + 
      geom_boxplot(width=.3, fill = wes_palette("Darjeeling2")[1]) + 
      theme_bw() + geom_beeswarm(groupOnX=FALSE, alpha = 0.5) + labs(y = "", x = "D-dimers (ng/ml)", subtitle = paste0("missing data for ", missing, " cases")) + lims(x = c(0,11000)) +
      geom_vline(xintercept = co_Ddim, linetype = "dashed", color = "black") 

Ddim_grid <- plot_grid(p_Ddim_cohort, p_Ddim_single, align = "v", nrow = 2, rel_heights = c(2/3, 1/3))
Ddim_grid
```


### Sodium

```{r}
collapse_cohort <- collapse_labvals_cohort(df_cohort_controls, "min", "sodium")
collapse_single <- collapse_labvals_single(df_singlecases, "min", "sodium")
missing <- sum(is.na(collapse_single$sodium_min))

p_sodium_cohort <- ggplot(collapse_cohort, aes(y = cohort_id, x = sodium_med, col = cohort_type)) + 
        geom_point() +  
        geom_errorbar(aes(xmin=sodium_min, xmax=sodium_max, col=cohort_type), width=.2, position=position_dodge(.9)) + lims(x = c(100,200)) + 
        theme_bw() + labs(title = "Sodium", y = "cohort", x = "")  +
        geom_vline(xintercept = co_sodium, linetype = "dashed", color = "black")  + theme(legend.justification = c(1, 1), legend.position = c(0.98, 0.98), legend.title=element_blank()) +
        scale_color_manual(values = wes_palette("Royal1"))

p_sodium_single <- ggplot(collapse_single, aes(x = as.numeric(sodium_min), y = cohort_id)) +
      geom_violin(fill = wes_palette("Darjeeling2")[4]) + 
      geom_boxplot(width=.3, fill = wes_palette("Darjeeling2")[1]) + 
      theme_bw() + geom_beeswarm(groupOnX=FALSE, alpha = 0.5) + labs(y = "", x = "Sodium (mmol/L)", subtitle = paste0("missing data for ", missing, " cases")) + lims(x = c(100,200)) +
      geom_vline(xintercept = co_sodium, linetype = "dashed", color = "black") 

sodium_grid <- plot_grid(p_sodium_cohort, p_sodium_single, align = "v", nrow = 2, rel_heights = c(2/3, 1/3))
sodium_grid
```


## Critical care interventions

### Inotropes
```{r}
makeBarplot(var_id_cohort = "critcare_inotrop_n", var_id_single = "critcare_inotrop", var_id = "inotropes")
```

```{r}
makeHeatmap_cohort("critcare", "critcare",exclude_single = "days", plottitle = "Cases receiving critical care interventions, per cohort")

barSymp("critcare", "critcare", exclude_single = "days", plottitle = "Cases receiving critical care interventions")
```

## Treatments
### IVIg
```{r}
makeBarplot(var_id_cohort = "rx_IVIg_once_n", var_id_single = "rx_IVIg_once", var_id = "IVIg")
```

### Overall therapy
```{r}
makeHeatmap_cohort("rx", "rx",exclude_single = "days", plottitle = "Cases receiving treatment, per cohort")


barSymp("rx", "rx", exclude_single = "days", plottitle = "Cases receiving treatment")
```


# Case definitions
## Lab reference values
Cut-offs in this study:

- Neutrophilia > 8000/µL
- Elevated CRP > 10 mg/L
- Lymphopenia < 1250/µL
- WBC > 11000/µL
- Fibrinogen > 400 mg/dL
- D-dimers > 250 ng/mL
- Ferritin > 300 ng/mL
- Albumin < 34 g/L
- Procalcitonin > 0.49 ng/mL
- LDH > 280 U/L
- IL6 > 16.4 pg/mL
- ESR > 22 mm/
- BNP > 100 pg/mL
- NTproBNP > 400 pg/mL
- Troponin > 0.04 ng/mL

## PIMS-TS
[Source RCPCH](https://www.rcpch.ac.uk/sites/default/files/2020-05/COVID-19-Paediatric-multisystem-%20inflammatory%20syndrome-20200501.pdf)  

1. A child presenting with persistent fever, inflammation (neutrophilia, elevated CRP and lymphopaenia) and evidence of single or multi-organ dysfunction (shock, cardiac, respiratory, renal, gastrointestinal or neurological disorder) with additional features (see listed in Appendix 1 ). This may include children fulfilling full or partial criteria for Kawasaki disease.
2. Exclusion of any other microbial cause, including bacterial sepsis, staphylococcal or streptococcal shock syndromes, infections associated with myocarditis such as enterovirus (waiting for results of these investigations should not delay seeking expert advice).
3. SARS-CoV-2 PCR testing may be positive or negative 

We are unable to evaluate criteria 2.

```{r, fig.height= 10, fig.width= 8}
PIMS_TS_fulfilled <- apply(df_singlecases, 1, function(row) {
    # persistent fever, inflammation (neutrophilia, elevated CRP and lymphopaenia) 
    pat_id <- row["patientID_int"]
    fever <- row["symp_fever"] == TRUE
    neutrophilia <- as.numeric(row["lab_neutrophils"]) > co_neutrophilia
    elevated_CRP <- (as.numeric(row["lab_CRP_admis"]) > co_CRP | as.numeric(row["lab_CRP_NS"]) > co_CRP | as.numeric(row["lab_CRP_peak"]) > co_CRP )
    lymphopenia <- as.numeric(row["lab_lymphocytes_lowest"]) < co_lympho
    inflamm <- any(fever, neutrophilia, elevated_CRP, lymphopenia)
    
    # lab values
    #fibrinogen <- row["lab_fibrino"] > co_fibrino
    #Ddimers <- row["lab_Ddim_peak"] > co_Ddim |  row["lab_Ddim_NS"] > co_Ddim
    #ferritin <- (row["lab_ferritin_NS"] > co_ferritin | row["lab_ferritin_admis"] > co_ferritin | row["lab_ferritin_peak"] > co_ferritin)
    #albumin <- row["lab_albumin_admis"] < co_albu | row["lab_albumin_lowest"] < co_albu | row["lab_albumin_NS"] < co_albu
    #lab_vals <- any(fibrinogen, Ddimers, ferritin, albumin)
    
    # single or multi-organ dysfunction (shock, cardiac, respiratory, renal, gastrointestinal or neurological disorder)
    pneumonia <- row["symp_resp_pneumonia"] == TRUE
    resp_failure <- row["symp_resp_failure"] == TRUE
    resp <- any(pneumonia, resp_failure)
    
    AKI <- row["symp_renal_AKI"] == TRUE
    RRT <- row["critcare_RRT"] == TRUE
    renal <- any(AKI, RRT)
    
    myocarditis <- row["symp_cardiovasc_myocard"] == TRUE
    pericarditis <- row["symp_cardiovasc_pericard"] == TRUE
    LVEF_under30 <- row["symp_cardiovasc_LV_less30"] == TRUE
    LVEF_30to55 <- row["symp_cardiovasc_LV_30to55"] == TRUE
    BNP <- (as.numeric(row["lab_BNP_admis"]) > co_BNP | as.numeric(row["lab_BNP_max"]) > co_BNP ) 
    NTproBNP <- as.numeric(row["lab_NTproBNP"]) > co_NTproBNP
    tropo <- as.numeric(row["lab_troponin_admis"]) > co_tropo
    shock <- row["symp_cardiovasc_shock"] == TRUE
    
    cardiovasc <- any(myocarditis, LVEF_under30, LVEF_30to55, NTproBNP, BNP, tropo, shock)
    
    rash <- row["kawasaki_exanthema"] == TRUE
    dermato <- any(rash)
    
    organ_dysfunc <- sum(resp, renal, cardiovasc, dermato, na.rm = TRUE) >= 1

    criteria_fulfilled <- (inflamm) & organ_dysfunc #&lab_vals
    #return(c(pat_id, "criteria1_inflamm" = inflamm, "criteria2_labvals" = lab_vals, "criteria3_organdysfunc" = organ_dysfunc, "criteria_fulfilled" = criteria_fulfilled))
    return(c(pat_id, "criteria1_inflamm" = inflamm, "criteria3_organdysfunc" = organ_dysfunc, "criteria_fulfilled" = criteria_fulfilled))
})

PIMS_TS_fulfilled <- PIMS_TS_fulfilled %>% t() %>% as_tibble()
PIMS_TS_fulfilled <- type_convert(PIMS_TS_fulfilled)
PIMS_TS_fulfilled_heatmap <- PIMS_TS_fulfilled
cols <- sapply(PIMS_TS_fulfilled_heatmap, is.logical)
PIMS_TS_fulfilled_heatmap[,cols] <- lapply(PIMS_TS_fulfilled_heatmap[,cols], as.numeric)
PIMS_TS_fulfilled_heatmap_melt <- PIMS_TS_fulfilled_heatmap %>% melt()
PIMS_TS_fulfilled_heatmap_melt[is.na(PIMS_TS_fulfilled_heatmap_melt)] <- 2

skim(PIMS_TS_fulfilled)

#ggplot(PIMS_TS_fulfilled_heatmap_melt, aes(x = variable, y = as.character(patientID_int), fill = as.factor(value))) + geom_tile() + theme_classic() + theme(axis.line=element_blank()) + labs(y = "Patient ID", x = "criteria", fill = "criteria met", title = "Overview of which single cases fulfill PIMS-TS case definition") +  scale_fill_manual(labels = c("No", "Yes", "Missing"), values = c("pink2", "royalblue3", "darkgrey")) + theme(axis.text.x=element_text(angle=90, hjust=1))
```

## CDC MIS-C
[Source CDC](https://www.cdc.gov/mis-c/hcp/) and [UpToDate](https://www.uptodate.com/contents/image?imageKey=PEDS%2F128201&topicKey=PEDS%2F127488)
The case definition for MIS-C is:

1. Age <21 years
2. Clinical presentation consistent with MIS-C, including all of the following:
    - Fever
        - Documented fever >38.0°C (100.4°F) for ≥24 hours or
        - Report of subjective fever lasting ≥24 hours
    - Laboratory evidence of inflammation
    - Severe illness requiring hospitalization
    - Multisystem involvement
        - 2 or more organ systems involved
            - Cardiovascular (eg, shock, elevated troponin, elevated BNP, abnormal echocardiogram, arrhythmia)
            - Respiratory (eg, pneumonia, ARDS, pulmonary embolism)
            - Renal (eg, AKI, renal failure)
            - Neurologic (eg, seizure, stroke, aseptic meningitis)
            - Hematologic (eg, coagulopathy)
            - Gastrointestinal (eg, elevated liver enzymes, diarrhea, ileus, gastrointestinal bleeding)
            - Dermatologic (eg, erythroderma, mucositis, other rash)
3. No alternative plausible diagnoses
4. Recent or current SARS-CoV-2 infection or exposure
    - Any of the following:
    - Positive SARS-CoV-2 RT-PCR
    - Positive serology
    - Positive antigen test
    - COVID-19 exposure within the 4 weeks prior to the onset of symptoms



```{r, fig.height= 10, fig.width= 8}

CDC_fulfilled <- apply(df_singlecases, 1, function(row) {
    # criteria 1
    criteria1 = TRUE
    
    # criteria 2
    pat_id <- row["patientID_int"]
    
    # fever?
    fever <- row["symp_fever"] == TRUE | row["kawasaki_fever"] == TRUE

    inflamm <- any(fever)
    
    # lab values evidence for inflammation
    neutrophilia <- as.numeric(row["lab_neutrophils"]) > co_neutrophilia
    elevated_CRP <- (as.numeric(row["lab_CRP_admis"]) > co_CRP | as.numeric(row["lab_CRP_NS"]) > co_CRP | as.numeric(row["lab_CRP_peak"]) > co_CRP )
    lymphopenia <- as.numeric(row["lab_lymphocytes_lowest"]) < co_lympho
    fibrinogen <- as.numeric(row["lab_fibrino"]) > co_fibrino
    Ddimers <- as.numeric(row["lab_Ddim_peak"]) > co_Ddim |  as.numeric(row["lab_Ddim_NS"]) > co_Ddim
    ferritin <- (as.numeric(row["lab_ferritin_NS"]) > co_ferritin | as.numeric(row["lab_ferritin_admis"]) > co_ferritin | as.numeric(row["lab_ferritin_peak"]) > co_ferritin)
    albumin <- as.numeric(row["lab_albumin_admis"]) < co_albu | as.numeric(row["lab_albumin_lowest"]) < co_albu | as.numeric(row["lab_albumin_NS"]) < co_albu
    PCT <- as.numeric(row["lab_PCT_admis"]) > co_PCT | as.numeric(row["lab_PCT_peak"]) > co_PCT | as.numeric(row["lab_PCT_NS"]) > co_PCT 
    LDH <- as.numeric(row["lab_LDH"]) > co_LDH
    IL6 <- as.numeric(row["lab_IL6"]) > co_IL6
    ESR <- as.numeric(row["lab_ESR"]) > co_ESR

    lab_vals <- any(neutrophilia, elevated_CRP, lymphopenia, fibrinogen, Ddimers, ferritin, albumin, PCT, LDH, IL6, ESR)
    
    # Ilness requiring hospitalisation
    ## used surrogate parameters for hosp
    hosp_ICU <- row["admis_hosp_days"] > 1 | row["admis_ICU_days"] > 1 | row["admis_PICU_admis"] == TRUE
    NIV <- row["critcare_NIV"] == TRUE | row["critcare_NIV_days"] > 1
    MV <- row["critcare_MV"] == TRUE | row["critcare_MV_days"] > 1
    inotrop <- row["critcare_inotrop"] == TRUE | row["critcare_inotrop_days"] > 1
    ECMO <- row["critcare_ECMO"] == TRUE 
    IVIg <- row["rx_IVIg_once"] == TRUE  |  row["rx_IVIg_multip"] == TRUE 
    biologicals <- row["rx_anakinra"] == TRUE | row["rx_tocilizumab"] == TRUE | row["rx_infliximab"] == TRUE | row["rx_antibiotics"] == TRUE | row["rx_plasma"] == TRUE | row["rx_remdesivir"] == TRUE 
    heparin <- row["rx_heparin"] == TRUE


    req_hosp <- any(hosp_ICU, NIV, MV, inotrop, ECMO, IVIg, biologicals, heparin)
    
    ## multisystem involvement >= 2
    ## respiratory
    pneumonia <- row["symp_resp_pneumonia"] == TRUE
    resp_failure <- row["symp_resp_failure"] == TRUE
    resp <- any(pneumonia, resp_failure)
    
    AKI <- row["symp_renal_AKI"] == TRUE
    RRT <- row["critcare_RRT"] == TRUE
    renal <- any(AKI, RRT)
    
    myocarditis <- row["symp_cardiovasc_myocard"] == TRUE
    pericarditis <- row["symp_cardiovasc_pericard"] == TRUE
    LVEF_under30 <- row["symp_cardiovasc_LV_less30"] == TRUE
    LVEF_30to55 <- row["symp_cardiovasc_LV_30to55"] == TRUE
    BNP <- (as.numeric(row["lab_BNP_admis"]) > co_BNP | as.numeric(row["lab_BNP_max"]) > co_BNP ) 
    NTproBNP <- as.numeric(row["lab_NTproBNP"]) > co_NTproBNP
    tropo <- as.numeric(row["lab_troponin_admis"]) > co_tropo
    shock <- row["symp_cardiovasc_shock"] == TRUE
    
    cardiovasc <- any(myocarditis, LVEF_under30, LVEF_30to55, NTproBNP, BNP, tropo, shock)
    
    rash <- row["kawasaki_exanthema"] == TRUE
    dermato <- any(rash)
    
    organ_dysfunc <- sum(resp, renal, cardiovasc, dermato, na.rm = TRUE) >= 2
    
    criteria2 <- sum(inflamm, lab_vals, req_hosp, organ_dysfunc, na.rm = TRUE) == 4
    # criteria 3
    ## not evaluable
    criteria3 = TRUE
    # criteria 4
    # COVID pos?
    PCR_pos <- row["covid_PCR_pos"] == TRUE
    stool_pos <- row["covid_PCR_stool_pos"] == TRUE
    closecontact <- row["covid_closecontact"] == TRUE
    IgA <- row["covid_IgA_pos"] == TRUE
    IgM <- row["covid_IgM_pos"] == TRUE    
    IgG <- row["covid_IgG_pos"] == TRUE    
    any_sero <- row["covid_sero_pos"] == TRUE
    
    criteria4 <- any(PCR_pos, stool_pos, closecontact, IgA, IgM, IgG, any_sero)
    
    if (FALSE %in% c(criteria1, criteria2, criteria3, criteria4)){
      criteria_fulfilled <- FALSE
    } else if (NA %in% c(criteria1, criteria2, criteria3, criteria4)){
      criteria_fulfilled <- NA
    } else if (sum(criteria1, criteria2, criteria3, criteria4, na.rm = TRUE) == 4){
      criteria_fulfilled <- TRUE
    }
    
    #criteria_fulfilled <- sum(criteria1, criteria2, criteria3, criteria4, na.rm = TRUE) == 4
    return(c(pat_id, "criteria1_age" = criteria1, "criteria2_clinical" = criteria2, "criteria3_noAlt" = criteria3, "criteria4_recentExposure" = criteria4, "criteria_fulfilled" = criteria_fulfilled))
})

CDC_fulfilled <- CDC_fulfilled %>% t() %>% as_tibble()
CDC_fulfilled <- type_convert(CDC_fulfilled)
CDC_fulfilled_heatmap <- CDC_fulfilled
cols <- sapply(CDC_fulfilled_heatmap, is.logical)
CDC_fulfilled_heatmap[,cols] <- lapply(CDC_fulfilled_heatmap[,cols], as.numeric)
CDC_fulfilled_heatmap_melt <- CDC_fulfilled_heatmap %>% melt()
CDC_fulfilled_heatmap_melt[is.na(CDC_fulfilled_heatmap_melt)] <- 2

skim(CDC_fulfilled)
#ggplot(CDC_fulfilled_heatmap_melt, aes(x = variable, y = as.character(patientID_int), fill = as.factor(value))) + geom_tile() + theme_classic() + theme(axis.line=element_blank()) + labs(y = "Patient ID", x = "criteria", fill = "criteria met", title = "Overview of which single cases fulfill CDC MIS-C case definition") +  scale_fill_manual(labels = c("No", "Yes", "Missing"), values = c("pink2", "royalblue3", "darkgrey")) + theme(axis.text.x=element_text(angle=90, hjust=1))
```

## WHO case definition
[Source UpToDate](https://www.uptodate.com/contents/image?imageKey=PEDS%2F128201&topicKey=PEDS%2F127488):

All 6 criteria must be met:

1. Age 0 to 19 years
2. Fever for ≥3 days
3. Clinical signs of multisystem involvement (at least 2 of the following):
    - Rash, bilateral nonpurulent conjunctivitis, or mucocutaneous inflammation signs (oral, hands, or feet)
    - Hypotension or shock
    - Cardiac dysfunction, pericarditis, valvulitis, or coronary abnormalities (including echocardiographic findings or elevated troponin/BNP)
    - Evidence of coagulopathy (prolonged PT or PTT; elevated D-dimer)
    - Acute gastrointestinal symptoms (diarrhea, vomiting, or abdominal pain)
4. Elevated markers of inflammation (eg, ESR, CRP, or procalcitonin)
5. No other obvious microbial cause of inflammation, including bacterial sepsis and staphylococcal/streptococcal toxic shock syndromes
6. Evidence of SARS-CoV-2 infection
    - Any of the following:
    - Positive SARS-CoV-2 RT-PCR
    - Positive serology
    - Positive antigen test
    - Contact with an individual with COVID-19

```{r, fig.height= 10, fig.width= 8}
#row <- df_singlecases[87, ]
WHO_fulfilled <- apply(df_singlecases, 1, function(row) {
    pat_id <- row["patientID_int"]
    
    # criteria 1
    criteria1 = TRUE
    
    # criteria 2: fever?
    fever <- row["symp_fever"] == TRUE | row["kawasaki_fever"] == TRUE

    criteria2 <- any(fever)
    
    # criteria 3: clinical signs of multisystem involvement (at least 2)
    ## Rash, bilateral nonpurulent conjunctivitis, or mucocutaneous inflammation signs (oral, hands, or feet)
    rash <- row["kawasaki_exanthema"] == TRUE
    conjunctivitis <- row["kawasaki_conjunctivitis"] == TRUE
    mucocutaneaous <- row["kawasaki_mouth"] == TRUE | row["kawasaki_extremity"] == TRUE
    
    criteria3_a <- any(rash, conjunctivitis, mucocutaneaous)
    
    ## hypotension or shock
    shock <- row["symp_cardiovasc_shock"] == TRUE
    criteria3_b <- any(shock)
    
    ## cardiac dysfunction
    myocarditis <- row["symp_cardiovasc_myocard"] == TRUE
    pericarditis <- row["symp_cardiovasc_pericard"] == TRUE
    LVEF_under30 <- row["symp_cardiovasc_LV_less30"] == TRUE
    LVEF_30to55 <- row["symp_cardiovasc_LV_30to55"] == TRUE
    BNP <- (as.numeric(row["lab_BNP_admis"]) > co_BNP | as.numeric(row["lab_BNP_max"]) > co_BNP ) 
    NTproBNP <- as.numeric(row["lab_NTproBNP"]) > co_NTproBNP
    tropo <- as.numeric(row["lab_troponin_admis"]) > co_tropo
    coronary <- row["symp_cardiovasc_cordilat"] == TRUE | row["symp_cardiovasc_aneurysm"] == TRUE
    
    criteria3_c <- any(myocarditis, LVEF_under30, LVEF_30to55, NTproBNP, BNP, tropo, coronary)
    
    ## coagulopathy
    fibrinogen <- as.numeric(row["lab_fibrino"]) > co_fibrino
    Ddimers <- as.numeric(row["lab_Ddim_peak"]) > co_Ddim |  as.numeric(row["lab_Ddim_NS"]) > co_Ddim
    
    criteria3_d <- any(fibrinogen, Ddimers)
    
    ## acute GI symptoms
    GIsymp <- row["symp_GI_NS"] == TRUE | row["symp_GI_abdopain"] == TRUE | row["symp_GI_vomiting"] == TRUE | row["symp_GI_diarrh"] == TRUE | row["symp_GI_colitis"] == TRUE 
    
    criteria3_e <- any(GIsymp)
    
    criteria3 <- sum(criteria3_a, criteria3_b, criteria3_c, criteria3_d, criteria3_e, na.rm = TRUE) >= 2
      
    # criteria 4: Elevated markers of inflammation (eg, ESR, CRP, or procalcitonin)
    neutrophilia <- as.numeric(row["lab_neutrophils"]) > co_neutrophilia
    elevated_CRP <- (as.numeric(row["lab_CRP_admis"]) >= co_CRP) | (as.numeric(row["lab_CRP_NS"]) >= co_CRP) | (as.numeric(row["lab_CRP_peak"]) >= co_CRP )
  #  print(paste0(pat_id, elevated_CRP, row["lab_CRP_peak"]))
    lymphopenia <- as.numeric(row["lab_lymphocytes_lowest"]) < co_lympho

    ferritin <- (as.numeric(row["lab_ferritin_NS"]) > co_ferritin | as.numeric(row["lab_ferritin_admis"]) > co_ferritin | as.numeric(row["lab_ferritin_peak"]) > co_ferritin)
    albumin <- as.numeric(row["lab_albumin_admis"]) < co_albu | as.numeric(row["lab_albumin_lowest"]) < co_albu | as.numeric(row["lab_albumin_NS"]) < co_albu
    PCT <- as.numeric(row["lab_PCT_admis"]) > co_PCT | as.numeric(row["lab_PCT_peak"]) > co_PCT | as.numeric(row["lab_PCT_NS"]) > co_PCT 
    LDH <- as.numeric(row["lab_LDH"]) > co_LDH
    IL6 <- as.numeric(row["lab_IL6"]) > co_IL6
    ESR <- as.numeric(row["lab_ESR"]) > co_ESR

    criteria4 <- any(neutrophilia, elevated_CRP, lymphopenia, ferritin, albumin, PCT, LDH, IL6, ESR)

    # criteria 5: No other obvious microbial cause of inflammation
    criteria5 <- TRUE
    
    # criteria 6: COVID pos?
    PCR_pos <- row["covid_PCR_pos"] == TRUE
    stool_pos <- row["covid_PCR_stool_pos"] == TRUE
    closecontact <- row["covid_closecontact"] == TRUE
    IgA <- row["covid_IgA_pos"] == TRUE
    IgM <- row["covid_IgM_pos"] == TRUE    
    IgG <- row["covid_IgG_pos"] == TRUE    
    any_sero <- row["covid_sero_pos"] == TRUE
    
    criteria6 <- any(PCR_pos, stool_pos, closecontact, IgA, IgM, IgG, any_sero)
    
    if (NA %in% c(criteria1, criteria2, criteria3, criteria4, criteria5, criteria6)){
      criteria_fulfilled <- NA
    } else if (FALSE %in% c(criteria1, criteria2, criteria3, criteria4, criteria5, criteria6)){
      criteria_fulfilled <- FALSE
    } else if (sum(criteria1, criteria2, criteria3, criteria4, criteria5, criteria6, na.rm = TRUE) == 6){
      criteria_fulfilled <- TRUE
    } else {
      criteria_fulfilled <- FALSE
    }

    return(c(pat_id, "criteria1_age" = criteria1, "criteria2_fever" = criteria2, "criteria3_clinical" = criteria3, "criteria4_inflamm" = criteria4, "criteria5_noAlt" = criteria5, "criteria6_recentExposure" = criteria6, "criteria_fulfilled" = criteria_fulfilled))
})


WHO_fulfilled <- WHO_fulfilled %>% t() %>% as_tibble()
WHO_fulfilled <- type_convert(WHO_fulfilled)
WHO_fulfilled_heatmap <- WHO_fulfilled
cols <- sapply(WHO_fulfilled_heatmap, is.logical)
WHO_fulfilled_heatmap[,cols] <- lapply(WHO_fulfilled_heatmap[,cols], as.numeric)
WHO_fulfilled_heatmap_melt <- WHO_fulfilled_heatmap %>% melt()
WHO_fulfilled_heatmap_melt[is.na(WHO_fulfilled_heatmap_melt)] <- 2

skim(WHO_fulfilled)

#ggplot(WHO_fulfilled_heatmap_melt, aes(x = variable, y = as.character(patientID_int), fill = as.factor(value))) + geom_tile() + theme_classic() + theme(axis.line=element_blank()) + labs(y = "Patient ID", x = "criteria", fill = "criteria met", title = "Overview of which single cases fulfill WHO MIS-C case definition") +  scale_fill_manual(labels = c("No", "Yes", "Missing"), values = c("pink2", "royalblue3", "darkgrey")) + theme(axis.text.x=element_text(angle=90, hjust=1))
```

## Per-case overview
```{r, fig.height = 10, fig.width=7}
PIMS_TS_fulfilled_heatmap_melt$criteria <- "PIMS-TS"
WHO_fulfilled_heatmap_melt$criteria <- "WHO"
CDC_fulfilled_heatmap_melt$criteria <- "CDC"

full_heatmap <- rbind(PIMS_TS_fulfilled_heatmap_melt, WHO_fulfilled_heatmap_melt, CDC_fulfilled_heatmap_melt)

ggplot(full_heatmap, aes(x = variable, y = as.character(patientID_int), fill = as.factor(value))) + geom_tile() + theme_classic() + theme(axis.line=element_blank()) + labs(y = "Patient ID", x = "criteria", fill = "criteria met", title = "Overview of which single cases fulfill case definitions") +  scale_fill_manual(labels = c("No", "Yes", "Missing"), values = wes_palette("Zissou1")) + theme(axis.text.x=element_text(angle=90, hjust=1)) + facet_wrap(~ criteria, scales = "free_x")


```


## Summary
```{r}
criteria_summary <- data.frame(PIMS_TS_fulfilled %>% select(criteria_fulfilled), CDC_fulfilled %>% select(criteria_fulfilled), WHO_fulfilled %>% select(criteria_fulfilled))
colnames(criteria_summary) <- c("PIMS-TS", "CDC", "WHO")

cols <- sapply(criteria_summary, is.logical)
criteria_summary[,cols] <- lapply(criteria_summary[,cols], as.numeric)

criteria_summary <- criteria_summary %>% melt() %>% 
                          group_by(variable) %>% 
                          summarise(fulfilled = sum(value == 1, na.rm = TRUE), not_fulfilled = sum(value == 0, na.rm = TRUE), not_evaluable = sum(is.na(value)))
criteria_summary$sum <- rowSums(criteria_summary[,-1])

criteria_summary_melt <- criteria_summary %>% melt()
colnames(criteria_summary_melt) <- c("center", "fulfilled", "count")

fill_bar <- ggplot(criteria_summary_melt %>% filter(fulfilled != 'sum'), aes(x = center, y = count, fill = fulfilled)) + 
      geom_bar(stat = "identity", position = "fill") + theme_bw() + 
      labs(y = "ratio", title = "Single cases meeting which criteria", subtitle = paste0("percent of total (n = ", max(criteria_summary_melt$count) ,")")) +
        scale_fill_manual(values = wes_palette("Royal1")[c(1,2,4)])

dodge_bar <- ggplot(criteria_summary_melt %>% filter(fulfilled != 'sum'), aes(x = center, y = count, fill = fulfilled)) + 
      geom_bar(stat = "identity", position = "dodge") + theme_bw() + 
      labs(y = "n", title = "Single cases meeting which criteria", subtitle = "absolute values") +
        scale_fill_manual(values = wes_palette("Royal1")[c(1,2,4)])

ggarrange(dodge_bar, fill_bar, legend = "bottom", common.legend = TRUE)
```

# Association of case definition with outcome
```{r}
WHO_outcome <- WHO_fulfilled_heatmap %>% select(contains("patientID_int") | contains("criteria_fulfilled"))
colnames(WHO_outcome) <- c("patientID_int", "casedef_WHO_fulfilled")

CDC_outcome <- CDC_fulfilled_heatmap %>% select(contains("patientID_int") | contains("criteria_fulfilled"))
colnames(CDC_outcome) <- c("patientID_int", "casedef_CDC_fulfilled")

PIMS_TS_outcome <- PIMS_TS_fulfilled_heatmap %>% select(contains("patientID_int") | contains("criteria_fulfilled"))
colnames(PIMS_TS_outcome) <- c("patientID_int", "casedef_PIMS_TS_fulfilled")

assoc_outcome <- merge(WHO_outcome, CDC_outcome, by = "patientID_int")
assoc_outcome <- merge(assoc_outcome, PIMS_TS_outcome)
#assoc_outcome <- assoc_outcome[complete.cases(assoc_outcome[ ,-1]),]

outcome_params <- df_singlecases %>% select(patientID_int | symp_cardiovasc_cordilat | symp_cardiovasc_aneurysm | symp_cardiovasc_shock | outcome_death | critcare_MV | critcare_ECMO)

assoc_outcome_full <- merge(outcome_params, assoc_outcome, by = "patientID_int", all = TRUE)

cols <- sapply(assoc_outcome_full, is.logical)
assoc_outcome_full[,cols] <- lapply(assoc_outcome_full[,cols], as.numeric)

makeUpsetR(assoc_outcome_full %>% select(-contains("patientID")))
```

A new variable 'unfavourable course' made, which contains the following:

- symp_cardiovasc_cordilat 
- symp_cardiovasc_aneurysm
- symp_cardiovasc_shock 
- outcome_death
- critcare_MV 
- critcare_ECMO
- critcare_RRT
- critcare_inotrop
- admis_PICU_admis

Mild presentation means all of the above are either 0 or NA. 

```{r}
assoc_outcome <- merge(WHO_outcome, CDC_outcome, by = "patientID_int")
assoc_outcome <- merge(assoc_outcome, PIMS_TS_outcome)
#assoc_outcome <- #assoc_outcome[complete.cases(assoc_outcome[ ,-1]),]

outcome_params <- df_singlecases %>% select(patientID_int | contains("critcare")  | contains("admis_PICU_admis") | contains("outcome_death")  |contains ("symp_cardiovasc_cordilat") | contains ("symp_cardiovasc_aneurysm")  |contains("symp_cardiovasc_shock"))

assoc_outcome_full <- merge(outcome_params, assoc_outcome, by = "patientID_int")

cols <- sapply(assoc_outcome_full, is.logical)
assoc_outcome_full[,cols] <- lapply(assoc_outcome_full[,cols], as.numeric)

assoc_outcome_full$unfavourable_course <- ifelse(assoc_outcome_full$symp_cardiovasc_cordilat == 1 | assoc_outcome_full$symp_cardiovasc_aneurysm == 1 | assoc_outcome_full$symp_cardiovasc_shock == 1 | assoc_outcome_full$outcome_death == 1 | assoc_outcome_full$critcare_MV == 1 | assoc_outcome_full$critcare_ECMO == 1 | assoc_outcome_full$critcare_RRT == 1 | assoc_outcome_full$admis_PICU_admis == 1 | assoc_outcome_full$critcare_inotrop == 1 , 1, 0)

assoc_outcome_full$mild_presentation <- ifelse((assoc_outcome_full$unfavourable_course == 0 | is.na(assoc_outcome_full$unfavourable_course)), 1, 0)

makeUpsetR(assoc_outcome_full %>% select(contains("casedef") | contains("unfavourable_course") ))


makeUpsetR(assoc_outcome_full %>% select(contains("casedef") | contains("unfavourable_course")  | contains("mild_pres") ))

```

A new variable 'PICU candidate' made, which contains the following:

- symp_cardiovasc_shock 
- outcome_death
- critcare_MV 
- critcare_ECMO
- critcare_RRT
- critcare_inotrop
- admis_PICU_admis

Mild presentation means all of the above are either 0 or NA. 

```{r}
assoc_outcome <- merge(WHO_outcome, CDC_outcome, by = "patientID_int")
assoc_outcome <- merge(assoc_outcome, PIMS_TS_outcome)
#assoc_outcome <- assoc_outcome[complete.cases(assoc_outcome[ ,-1]),]

outcome_params <- df_singlecases %>% select(patientID_int | contains("critcare")  | contains("admis_PICU_admis") | contains("outcome_death")  |contains ("symp_cardiovasc_cordilat") | contains ("symp_cardiovasc_aneurysm")  |contains("symp_cardiovasc_shock"))

assoc_outcome_full <- merge(outcome_params, assoc_outcome, by = "patientID_int")

cols <- sapply(assoc_outcome_full, is.logical)
assoc_outcome_full[,cols] <- lapply(assoc_outcome_full[,cols], as.numeric)

assoc_outcome_full$PICU_candidate <- ifelse( assoc_outcome_full$symp_cardiovasc_shock == 1 | assoc_outcome_full$outcome_death == 1 | assoc_outcome_full$critcare_MV == 1 | assoc_outcome_full$critcare_ECMO == 1 | assoc_outcome_full$critcare_RRT == 1 | assoc_outcome_full$admis_PICU_admis == 1 | assoc_outcome_full$critcare_inotrop == 1 , 1, 0)

assoc_outcome_full$mild_presentation <- ifelse((assoc_outcome_full$PICU_candidate == 0 | is.na(assoc_outcome_full$PICU_candidate)), 1, 0)

makeUpsetR(assoc_outcome_full %>% select(contains("casedef") | contains("PICU_candidate") ))


makeUpsetR(assoc_outcome_full %>% select(contains("casedef") | contains("PICU_candidate")  | contains("mild_pres") ))

```


# Final figures

## Sex
```{r}
var_cohort <- df_cohort %>% select(contains("sex") | ("cohort_id") | "tot_cases_n")
var_cohort$cohort_id <- paste0(var_cohort$cohort_id, " (n = ", var_cohort$tot_cases_n,")")
sex_f <- var_cohort %>% group_by(cohort_id) %>% summarize(prct = sex_f/tot_cases_n) %>%  mutate(sex = "female")
sex_m <- var_cohort %>% group_by(cohort_id) %>% summarize(prct = sex_m/tot_cases_n) %>% mutate(sex = "male")
sex_all <- rbind(sex_f, sex_m)

p_sex_cohort <- ggplot(sex_all, aes(y = cohort_id, x = prct, fill = sex)) + 
  geom_bar(stat = "identity", position = "fill") + 
  theme_bw() + labs(x = "") +  labs(y = "") +
  scale_fill_manual(values = wes_palette("Royal1")) + theme(legend.position = "top", legend.title=element_blank())+
  rremove("y.text") 

var_controls <- df_cohort_controls %>% filter(cohort_type == "control") %>% select(contains("sex") | ("cohort_id") | "tot_cases_n")
var_controls$cohort_id <- paste0(var_controls$cohort_id, " (n = ", var_controls$tot_cases_n,")")
sex_f <- var_controls %>% group_by(cohort_id) %>% summarize(prct = sex_f/tot_cases_n) %>% mutate(sex = "female")
sex_m <- var_controls %>% group_by(cohort_id) %>% summarize(prct = sex_m/tot_cases_n) %>% mutate(sex = "male")
sex_all <- rbind(sex_f, sex_m)

p_sex_controls <- ggplot(sex_all, aes(y = cohort_id, x = prct, fill = sex)) + 
  geom_bar(stat = "identity", position = "fill") + 
  theme_bw() + labs(x = "") + 
  scale_fill_manual(values = wes_palette("Royal1"))+
  theme(legend.position = "none")  + labs(y = "")+
  rremove("y.text") 

n_single <- df_singlecases %>% nrow()
var_single <- df_singlecases %>% select(contains("sex"))
var_single$sex_m <- ifelse(var_single$sex == "M", TRUE, FALSE)
var_single$sex_f <- ifelse(var_single$sex == "F", TRUE, FALSE)
cols <- sapply(var_single, is.logical)
var_single[,cols] <- lapply(var_single[,cols], as.numeric)
var_single <- colSums(var_single %>% select(-sex), na.rm = TRUE)
var_single <- var_single/nrow(df_singlecases)*100

sex_single <- data.frame(cohort_id = paste0("single cases (n = ", n_single_cases, ")"), prct = c(var_single["sex_m"], var_single["sex_f"]), sex = c("male", "female"))

p_sex_single <- ggplot(sex_single, aes(y = cohort_id, x = prct, fill = sex)) + 
  geom_bar(stat = "identity", position = "fill") + 
  theme_bw() + 
  scale_fill_manual(values = wes_palette("Royal1"))+
  theme(legend.position = "none") + labs(y = "", x = "Fraction")+ rremove("y.text") 

plot_sex <- plot_grid(p_sex_cohort, p_sex_controls, p_sex_single, align = "v", nrow = 3, rel_heights = c(2/3, 1/5, 1/3))
plot_sex
```

## Age distribution

```{r}
cohort_age <- df_cohort_controls %>% select(contains("cohort_id") | contains("age") | contains("cohort_type")  | contains("tot_cases_n"))
cohort_age$cohort_id <- paste0(cohort_age$cohort_id, " (n = ", cohort_age$tot_cases_n,")")
cohort_age$age_med_yrs <- as.numeric(cohort_age$age_med_yrs )
cohort_age$age_Q1_yrs <- as.numeric(cohort_age$age_Q1_yrs)
cohort_age$age_Q3_yrs <- as.numeric(cohort_age$age_Q3_yrs)
cohort_age$age_min_yrs <- as.numeric(cohort_age$age_min_yrs)
cohort_age$age_max_yrs <- as.numeric(cohort_age$age_max_yrs)

cohort_age$data_descr <- ifelse(!is.na(cohort_age$age_Q1_yrs) & is.na(cohort_age$age_min_yrs) , "IQR", 
                                ifelse(is.na(cohort_age$age_Q1_yrs) & !is.na(cohort_age$age_min_yrs), "range", 
                                       ifelse(!is.na(cohort_age$age_Q1_yrs) & !is.na(cohort_age$age_min_yrs), "IQR + range", "none")))

p_age_cohort <- ggplot(cohort_age %>% filter(cohort_type == "covid"), aes(y = cohort_id, x = age_med_yrs, col = data_descr)) + 
  geom_point(size = 4) + 
  geom_errorbar(aes(xmin=age_Q1_yrs, xmax=age_Q3_yrs), width=.8, position=position_dodge(.9)) +
  geom_errorbar(aes(xmin=age_min_yrs,  xmax=age_max_yrs), width=.2, position=position_dodge(.9)) +
  theme_bw() + lims(x = c(0,21)) + 
  labs(y = "", x = "", col = "bars") + theme(legend.position="top", legend.title=element_blank())+
  scale_color_manual(values = c(wes_palette("BottleRocket2")[1:3], wes_palette("BottleRocket1")[2]))

p_age_controls <- ggplot(cohort_age %>% filter(cohort_type != "covid"), aes(y = cohort_id, x = age_med_yrs, col = data_descr)) + 
  geom_point(size = 4) + 
  geom_errorbar(aes(xmin=age_Q1_yrs, xmax=age_Q3_yrs), width=.2, position=position_dodge(.9)) +
  geom_errorbar(aes(xmin=age_min_yrs,  xmax=age_max_yrs), width=.2, position=position_dodge(.9)) +
  theme_bw() + lims(x = c(0,21)) +
  labs(y = "", x = "", col = "bars") + theme(legend.position="none")+
  scale_color_manual(values = wes_palette("BottleRocket2")[1])

p_age_single <- ggplot(df_singlecases, aes(x = as.numeric(age), y = paste0("single cases (n = ", n_single,")"))) +
  geom_violin(fill = wes_palette("Darjeeling2")[4]) + 
  geom_boxplot(width=.3, fill = wes_palette("Darjeeling2")[1]) + 
  theme_bw() + geom_beeswarm(groupOnX=FALSE, alpha = 0.5) + lims(x = c(0,21)) + 
  labs(y = "", x = "Age (years)")

plot_age <- plot_grid(p_age_cohort, p_age_controls, p_age_single, align = "v", nrow = 3, rel_heights = c(2/3, 1/5, 1/3))
plot_age
```


```{r, fig.height= 10, fig.width=16}
figure <- ggarrange(plot_age, plot_sex, labels = c("A", "B"), widths = c(1.25,1))
ggsave(figure, filename = "./plots/demo_grid_plots.png", dpi = 300, height=7, width=10)
ggsave(figure, filename = "./plots/demo_grid_plots.svg", dpi = 300, height=7, width=10)
ggsave(figure, filename = "./plots/demo_grid_plots.pdf", dpi = 300, height=7, width=10)
figure
```


## Lab values
### C-reactive protein

```{r}
crp_collapse_cohort <- collapse_labvals_cohort(df_cohort_controls, "max", "CRP")
crp_collapse_single <- collapse_labvals_single(df_singlecases, "max", "CRP")
crp_missing <- sum(is.na(crp_collapse_single$CRP_max))

p_crp_cohort <- ggplot(crp_collapse_cohort, aes(y = cohort_id, x = CRP_med, col = cohort_type)) + 
  geom_point() +  
  geom_errorbar(aes(xmin=CRP_min, xmax=CRP_max), width=.2, position=position_dodge(.9)) + lims(x = c(0,600)) + 
  theme_bw() + labs(title = "CRP", y = "cohort", x = "") +
  geom_vline(xintercept = co_CRP, linetype = "dashed", color = "black") + theme(legend.justification = c(1, 1), legend.position = c(0.98, 0.98), legend.title=element_blank()) +
  scale_color_manual(values = wes_palette("Royal1"))

p_crp_single <- ggplot(crp_collapse_single, aes(x = as.numeric(CRP_max), y = cohort_id)) +
  geom_violin(fill = wes_palette("Darjeeling2")[4]) + 
  geom_boxplot(width=.3, fill =  wes_palette("Darjeeling2")[1]) + 
  theme_bw() + geom_beeswarm(groupOnX=FALSE, alpha = 0.5) + lims(x = c(0,600)) + labs(y = "", x = "CRP (mg/dL)", subtitle = paste0("missing data for ", crp_missing, " cases")) +
  geom_hline(yintercept = co_CRP, linetype = "dashed", color = "black")

CRP_grid <- plot_grid(p_crp_cohort, p_crp_single, align = "v", nrow = 2, rel_heights = c(2/3, 1/3))
CRP_grid
```

### Ferritin
```{r}
ferritin_collapse_cohort <- collapse_labvals_cohort(df_cohort_controls, "max", "ferrit")
ferritin_collapse_single <- collapse_labvals_single(df_singlecases, "max", "ferrit")
ferritin_missing <- sum(is.na(ferritin_collapse_single$ferrit_max))

p_ferritin_cohort <- ggplot(ferritin_collapse_cohort, aes(y = cohort_id, x = ferrit_med, col = cohort_type)) + 
  geom_point() +  
  geom_errorbar(aes(xmin=ferrit_min, xmax=ferrit_max), width=.2, position=position_dodge(.9)) + lims(x = c(0,11000)) + 
  theme_bw() + labs(title = "Ferritin", y = "cohort", x = "") +
  geom_vline(xintercept = co_ferritin, linetype = "dashed", color = "black") + theme(legend.justification = c(1, 1), legend.position = "none", legend.title=element_blank()) +
  scale_color_manual(values = wes_palette("Royal1"))

p_ferritin_single <- ggplot(ferritin_collapse_single, aes(x = as.numeric(ferrit_max), y = cohort_id)) +
  geom_violin(fill = wes_palette("Darjeeling2")[4]) + 
  geom_boxplot(width=.3, fill = wes_palette("Darjeeling2")[1]) + 
  theme_bw() + geom_beeswarm(groupOnX=FALSE, alpha = 0.5) + labs(y = "", x = "Ferritin (µg/l)", subtitle = paste0("missing data for ", ferritin_missing, " cases")) + lims(x = c(0,11000)) +
  geom_vline(xintercept = co_ferritin, linetype = "dashed", color = "black") 

ferritin_grid <- plot_grid(p_ferritin_cohort, p_ferritin_single, align = "v", nrow = 2, rel_heights = c(2/3, 1/3))
ferritin_grid
```


### IL-6
Note: The cases from Pouletty et al are added to the single cases as they report on IL6 values. 

```{r}
IL6_collapse_cohort <- collapse_labvals_cohort(df_cohort_controls, "max", "IL6")
IL6_collapse_single <- collapse_labvals_single(df_singlecases_inclPouletty, "max", "IL6")
IL6_missing <- sum(is.na(IL6_collapse_single$IL6_max))

p_IL6_cohort <- ggplot(IL6_collapse_cohort, aes(y = cohort_id, x = IL6_med, col = cohort_type)) + 
  geom_point() +  
  geom_errorbar(aes(xmin=IL6_min, xmax=IL6_max), width=.2, position=position_dodge(.9)) + lims(x = c(0,2500)) + 
  theme_bw() + labs(title = "IL6", y = "cohort", x = "")  +
  geom_vline(xintercept = co_IL6, linetype = "dashed", color = "black")  + theme(legend.justification = c(1, 1), legend.position = "none", legend.title=element_blank()) +
  scale_color_manual(values = wes_palette("Royal1"))

p_IL6_single <- ggplot(IL6_collapse_single, aes(x = as.numeric(IL6_max), y = cohort_id)) +
  geom_violin(fill = wes_palette("Darjeeling2")[4]) + 
  geom_boxplot(width=.3, fill = wes_palette("Darjeeling2")[1]) + 
  theme_bw() + geom_beeswarm(groupOnX=FALSE, alpha = 0.5) + labs(y = "", x = "IL6 (pg/ml)", subtitle = paste0("missing data for ", IL6_missing, " cases")) + lims(x = c(0,2500))  +
  geom_vline(xintercept = co_IL6, linetype = "dashed", color = "black") 

IL6_grid <- plot_grid(p_IL6_cohort, p_IL6_single, align = "v", nrow = 2, rel_heights = c(2/3, 1/3))
IL6_grid
```


### White blood cells
```{r}
wbc_collapse_cohort <- collapse_labvals_cohort(df_cohort_controls, "max", "WBC")
wbc_collapse_single <- collapse_labvals_single(df_singlecases, "max", "WBC")
wbc_missing <- sum(is.na(wbc_collapse_single$WBC_max))

p_wbc_cohort <- ggplot(wbc_collapse_cohort, aes(y = cohort_id, x = WBC_med, col = cohort_type)) + 
  geom_point() +  
  geom_errorbar(aes(xmin=WBC_min, xmax=WBC_max), width=.2, position=position_dodge(.9)) + lims(x = c(0,50000)) + 
  theme_bw() + labs(title = "White blood cells", y = "", x = "")  +
  geom_vline(xintercept = co_WBC, linetype = "dashed", color = "black")  + theme(legend.justification = c(1, 1), legend.position = "none", legend.title=element_blank()) +
  scale_color_manual(values = wes_palette("Royal1"))+ rremove("y.text") 

p_wbc_single <- ggplot(wbc_collapse_single, aes(x = as.numeric(WBC_max), y = cohort_id)) +
  geom_violin(fill = wes_palette("Darjeeling2")[4]) + 
  geom_boxplot(width=.3, fill = wes_palette("Darjeeling2")[1]) + 
  theme_bw() + geom_beeswarm(groupOnX=FALSE, alpha = 0.5) + labs(y = "", x = "WBC (/µL)", subtitle = paste0("missing data for ", wbc_missing, " cases")) + lims(x = c(0,50000)) +
  geom_vline(xintercept = co_WBC, linetype = "dashed", color = "black") + rremove("y.text") 

WBC_grid <- plot_grid(p_wbc_cohort, p_wbc_single, align = "v", nrow = 2, rel_heights = c(2/3, 1/3))
WBC_grid
```

### Lymphocytes
```{r}
lympho_collapse_cohort <- collapse_labvals_cohort(df_cohort_controls, "min", "lympho")
lympho_collapse_single <- collapse_labvals_single(df_singlecases, "min", "lympho")
lympho_missing <- sum(is.na(lympho_collapse_single$lympho_min))

p_lympho_cohort <- ggplot(lympho_collapse_cohort, aes(y = cohort_id, x = lympho_med, col = cohort_type)) + 
  geom_point() +  
  geom_errorbar(aes(xmin=lympho_min, xmax=lympho_max), width=.2, position=position_dodge(.9)) + 
  theme_bw() + labs(title = "Lymphocytes", y = "", x = "") + lims(x = c(0,7500))  +
  geom_vline(xintercept = co_lympho, linetype = "dashed", color = "black") + theme(legend.justification = c(1, 1), legend.position = "none", legend.title=element_blank()) +
  scale_color_manual(values = wes_palette("Royal1"))+
  rremove("y.text") 

p_lympho_single <- ggplot(lympho_collapse_single, aes(x = as.numeric(lympho_min), y = cohort_id)) +
  geom_violin(fill = wes_palette("Darjeeling2")[4]) + 
  geom_boxplot(width=.3, fill = wes_palette("Darjeeling2")[1]) + 
  lims(x = c(0,7500))+
  theme_bw() + geom_beeswarm(groupOnX=FALSE, alpha = 0.5)  + labs(y = "", x = "Lymphocytes (/µL)", subtitle = paste0("missing data for ", lympho_missing, " cases")) +
  geom_vline(xintercept = co_lympho, linetype = "dashed", color = "black") +  rremove("y.text") 

lympho_grid <- plot_grid(p_lympho_cohort, p_lympho_single, align = "v", nrow = 2, rel_heights = c(2/3, 1/3))
lympho_grid
```


### Troponin
```{r}
troponin_collapse_cohort <- collapse_labvals_cohort(df_cohort_controls, "max", "troponin")
troponin_collapse_single <- collapse_labvals_single(df_singlecases, "max", "troponin")
troponin_missing <- sum(is.na(troponin_collapse_single$troponin_max))

p_troponin_cohort <- ggplot(troponin_collapse_cohort, aes(y = cohort_id, x = troponin_med, col = cohort_type)) + 
  geom_point() +  
  geom_errorbar(aes(xmin=troponin_min, xmax=troponin_max), width=.2, position=position_dodge(.9)) + lims(x = c(0,7000)) + 
  theme_bw() + labs(title = "Troponin", y = "", x = "")  +
  geom_vline(xintercept = co_tropo, linetype = "dashed", color = "black")  + theme(legend.justification = c(1, 1), legend.position = "none", legend.title=element_blank()) +
  scale_color_manual(values = wes_palette("Royal1"))+ rremove("y.text") 

p_troponin_single <- ggplot(troponin_collapse_single, aes(x = as.numeric(troponin_max), y = cohort_id)) +
  geom_violin(fill = wes_palette("Darjeeling2")[4]) + 
  geom_boxplot(width=.3, fill = wes_palette("Darjeeling2")[1]) + 
  theme_bw() + geom_beeswarm(groupOnX=FALSE, alpha = 0.5) + labs(y = "", x = "Troponin (ng/L)", subtitle = paste0("missing data for ", troponin_missing, " cases")) + lims(x = c(0,7000)) +
  geom_vline(xintercept = co_tropo, linetype = "dashed", color = "black") + rremove("y.text") 

troponin_grid <- plot_grid(p_troponin_cohort, p_troponin_single, align = "v", nrow = 2, rel_heights = c(2/3, 1/3))
troponin_grid
```


### Platelets

```{r}
collapse_cohort <- collapse_labvals_cohort(df_cohort_controls, "min", "platelet")
collapse_single <- collapse_labvals_single(df_singlecases, "min", "platelet")
missing <- sum(is.na(collapse_single$platelet_min))

p_platelet_cohort <- ggplot(collapse_cohort, aes(y = cohort_id, x = platelet_med/1000, col = cohort_type)) + 
  geom_point() +  
  geom_errorbar(aes(xmin=platelet_min/1000, xmax=platelet_max/1000, col=cohort_type), width=.2, position=position_dodge(.9)) + lims(x = c(0,750)) + 
  theme_bw() + labs(title = "Platelets", y = "", x = "")  +
  geom_vline(xintercept = co_platelet/1000, linetype = "dashed", color = "black")  + theme(legend.justification = c(1, 1), legend.position = "none", legend.title=element_blank()) +
  scale_color_manual(values = wes_palette("Royal1"))+ rremove("y.text") 

p_platelet_single <- ggplot(collapse_single, aes(x = as.numeric(platelet_min)/1000, y = cohort_id)) +
  geom_violin(fill = wes_palette("Darjeeling2")[4]) + 
  geom_boxplot(width=.3, fill = wes_palette("Darjeeling2")[1]) + 
  theme_bw() + geom_beeswarm(groupOnX=FALSE, alpha = 0.5) + labs(y = "", x = "Platelets (x1000/µL)", subtitle = paste0("missing data for ", missing, " cases")) + lims(x = c(0,750)) +
  geom_vline(xintercept = co_platelet, linetype = "dashed", color = "black") + rremove("y.text") 

platelet_grid <- plot_grid(p_platelet_cohort, p_platelet_single, align = "v", nrow = 2, rel_heights = c(2/3, 1/3))
platelet_grid
```


### D-dimers

```{r}
collapse_cohort <- collapse_labvals_cohort(df_cohort_controls, "max", "Ddim")
collapse_single <- collapse_labvals_single(df_singlecases, "max", "Ddim")
missing <- sum(is.na(collapse_single$Ddim_max))

p_Ddim_cohort <- ggplot(collapse_cohort, aes(y = cohort_id, x = Ddim_med, col = cohort_type)) + 
  geom_point() +  
  geom_errorbar(aes(xmin=Ddim_min, xmax=Ddim_max, col=cohort_type), width=.2, position=position_dodge(.9)) + lims(x = c(0,11000)) + 
  theme_bw() + labs(title = "D-dimers", y = "", x = "")  +
  geom_vline(xintercept = co_Ddim, linetype = "dashed", color = "black")  + theme(legend.justification = c(1, 1), legend.position = "none", legend.title=element_blank()) +
  scale_color_manual(values = wes_palette("Royal1")) + rremove("y.text") 

p_Ddim_single <- ggplot(collapse_single, aes(x = as.numeric(Ddim_max), y = cohort_id)) +
  geom_violin(fill = wes_palette("Darjeeling2")[4]) + 
  geom_boxplot(width=.3, fill = wes_palette("Darjeeling2")[1]) + 
  theme_bw() + geom_beeswarm(groupOnX=FALSE, alpha = 0.5) + labs(y = "", x = "D-dimers (ng/ml)", subtitle = paste0("missing data for ", missing, " cases")) + lims(x = c(0,11000)) +
  geom_vline(xintercept = co_Ddim, linetype = "dashed", color = "black") + rremove("y.text") 

Ddim_grid <- plot_grid(p_Ddim_cohort, p_Ddim_single, align = "v", nrow = 2, rel_heights = c(2/3, 1/3))
Ddim_grid
```


### Sodium

```{r}
collapse_cohort <- collapse_labvals_cohort(df_cohort_controls, "min", "sodium")
collapse_single <- collapse_labvals_single(df_singlecases, "min", "sodium")
missing <- sum(is.na(collapse_single$sodium_min))

p_sodium_cohort <- ggplot(collapse_cohort, aes(y = cohort_id, x = sodium_med, col = cohort_type)) + 
  geom_point() +  
  geom_errorbar(aes(xmin=sodium_min, xmax=sodium_max, col=cohort_type), width=.2, position=position_dodge(.9)) + lims(x = c(100,150)) + 
  theme_bw() + labs(title = "Sodium", y = "", x = "")  +
  geom_vline(xintercept = co_sodium, linetype = "dashed", color = "black")  + theme(legend.justification = c(1, 1), legend.position = "none", legend.title=element_blank()) +
  scale_color_manual(values = wes_palette("Royal1"))+ rremove("y.text") 

p_sodium_single <- ggplot(collapse_single, aes(x = as.numeric(sodium_min), y = cohort_id)) +
  geom_violin(fill = wes_palette("Darjeeling2")[4]) + 
  geom_boxplot(width=.3, fill = wes_palette("Darjeeling2")[1]) + 
  theme_bw() + geom_beeswarm(groupOnX=FALSE, alpha = 0.5) + labs(y = "", col = "", x = "Sodium (mmol/L)", subtitle = paste0("missing data for ", missing, " cases")) + lims(x = c(100,150)) +
  geom_vline(xintercept = co_sodium, linetype = "dashed", color = "black") + rremove("y.text") 

sodium_grid <- plot_grid(p_sodium_cohort, p_sodium_single, align = "v", nrow = 2, rel_heights = c(2/3, 1/3))
sodium_grid
```


### Grid plots
Example:
  
  Dashed lines equals reference value cut-off.

```{r, fig.height= 16, fig.width=12}
figure <- ggarrange(CRP_grid, WBC_grid, sodium_grid, ferritin_grid, lympho_grid, Ddim_grid, IL6_grid, platelet_grid, troponin_grid, labels = c("A", "B", "C", "D", "E", "G", "G", "H", "I"), widths = c(1.25,1,1))
ggsave(figure, filename = "./plots/lab_grid_plots.png", dpi = 300, height=16, width=12)
ggsave(figure, filename = "./plots/lab_grid_plots.svg", dpi = 300, height=12, width=16)
ggsave(figure, filename = "./plots/lab_grid_plots.pdf", dpi = 300, height=12, width=16)
figure
```

# SessionInfo
```{r}
sessionInfo()
```